>> Narrator: TheCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) (logo background tingles) >> Hey everyone, it's so good to see you, welcome back to theCube's day two coverage of MWC 23. We are live in Barcelona, Lisa Martin with Dave Nicholson, Dave we have had no signage of people dropping out, this conference is absolutely jam packed. There's so much interest in the industry, you've had a lot of interviews this morning, before we introduce our guests and have a great conversation about the industry and challenges and how they're being solved, what are some of the things that stuck out to you in conversations today? >> Well, I think the interesting, kind of umbrella conversation, that seems to be overlapping you know, overlying everything is this question about Open RAN and open standards in radio access network technology and where the operators of networks and the providers of technology come together to chart a better path forward. A lot of discussion of private 5G networks, it's very interesting, I think I've said this a few times, from a consumer's perspective, we feel like 5G has been with us for a long time- >> We do. >> But it's very clear that this, that we're really at the beginning of stages of this and I'm super excited for our guests that we have here because we're going to be able to talk to an actual operator- >> Yes. >> And hear what they have to say, we've heard a lot of people talking about the cool stuff they build, but we're going to get to hear from someone who actually works with this stuff, so- >> Who actually built it, absolutely. Please welcome our two guests, we have Ihab Tarazi CTO and SVP at Dell Technologies, and Juan Carlos Garcia SVP Technology Innovation and Ecosystems at Telephonica, it's great to have you guys on the program. >> So, thank you very much. >> So the buzz around this conference is incredible, 80,000 plus people, 2000 exhibitors, it's standing room only. Lot of opportunity in the industry, a lot of challenges though, Juan Carlos we'd love to get your perspective on, what are some of the industry challenges that Telephonica has faced that your peers are probably facing as well? >> Well we have two kinds of challenges, one is a business challenge, I would say that we may find in other industries, like profitability and growth and I will talk about it. And the second challenge is our technology challenge, we need the network to be ready to embrace a new wave of technologies and applications that are, you know, very demanding in terms of network characteristics and features. On the efficiency and profitability and growth, the solution comes as a challenge from changing the way networks are built and operated, from the traditional way to make them become software platforms. And this is not just at the knowledge challenge, it's also changing the mindset of network operators from a network and service provider to a digital service provider, okay? And this means several things, your network needs to become software-based so that you can manage it digitally and on top of it, you need to be able to deliver detail services digitally, okay? So there are three aspects, making your network so (indistinct) and cloud and cloud waste and then be able to sell in a different way to our customers. >> So some pretty significant challenges, but to your point, Juan Carlos, you share some of those challenges with other industries so there's some commonality there. I wanted to bring Ihab into the conversation, from Dell's perspective, we're seeing, you know, the explosion of data. Every company has to be a data company, we expect to have access to data in real time, if it's a new app, whatever it is. What are some of the challenges that you're seeing from your seat at Dell? >> Yeah, I think Juan Carlos explained that really well, what all the operators are talking about here between new applications, think metaverse, think video streaming, going all the way to the edge, think all the automation of factories and everything that's happening. It's not only requiring a whole new model for delivery and for building networks, but it's throwing out enormous amount of data and the data needs to be acted on to get the value of it. So the challenge is how do I collect the data? How do I catalog it? How do I make it usable? And then how do I make it persistent? So you know, it's high performance data storage and then after that, how do I move it to where I want to and be able to use it. And for many applications that has to happen in milliseconds for the value to come out. So now we've seen this before with enterprise but now I would say this digital transformation is happening at very large scale for all the telcos and starting to deal with very familiar themes we've seen before. >> So Juan Carlos, Telephonica, you hear from partners, vendors that they've done this before, don't worry, you're in good hands. >> Juan Carlos: Yeah, yeah. >> But as a practical matter, when you look at the challenges that you have and you think about the things you'll do to address them as you move forward, what are the immediate short term priorities? >> Okay. >> Versus the longer term priorities? What's realistic? You have a network to operate- >> Yeah. >> You're not just building something out of nothing, so you have to keep the lights on. >> Yeah. >> And you have to innovate, we call that by the way, in the CTO trade, ambidextrous, management using both hands, so what's your order of priorities? >> Well, the first thing, new technologies you are getting into the network need to come with a detail shape, so being cloud native, working by software. On the legacies that you need to keep alive, you need to go for a program to switch (indistinct) off progressively, okay? In fact, in Spain we are going to switch up the copper network in two years, so in 2024, Telephonica will celebrate 100 years and the celebration will be switching up the copper network and we'll have on the fixed access only fiber, okay. So more than likely, the network is necessary, all this digitalization may happen only on the new technologies because the new technologies are cloud-based, cloud native, become already ready for this digitalization process. And not only that, so you need also to build new things, we need an abstraction layer on top of the physical infrastructure to be able to manage the network by software, okay. This is something that happened in the computing world, okay, where the servers, you know, were covered with a cloud stack layer and we are doing the same thing in the network. We are trained to abstract the network services and capabilities and be able to offer them digitally to our customers. And this is a process that we are ongoing with many initiatives in the market, so one was the CAMARA community that was opened in Linux Foundation and the other one was the announcement we made yesterday of the open gateway initiative here at Mobile World Congress where all telecom operators have agreed to launch in this year a set of service APIs that are common worldwide, okay. This is a similar thing to what we did with 2G 35 years ago, to agree on a standard way of delivering a service and in this case is digital services based on APIs. >> What's the net result of? What are the benefits of having those open standards? Is it a benefit that myself as a consumer would enjoy? It seems, I mean, I've been, I'm old enough to remember, you know, a time before cellular telephones and I remember a time when it was very, very difficult to travel from North America to Europe with a cell phone. Now I land and my provider says, "Hey, welcome-" >> Juan Carlos: Yes. >> "Welcome, we're going to charge you a little extra money." And I say, "Hallelujah, awesome." So is part of that interoperability a benefit to consumers or, how, what? >> Yeah, you touch the right point. So in the same way you travel anywhere and you want to still make a call and send an SMS and connect to the internet, you will like your applications in your smartphone to work being them edge applications, okay, and these applications, each application will have to work to be executed very close to where you are, in a way that if you travel abroad the visitor network is serving you, okay. So this means that we are somehow extending the current interconnection and roaming agreements between operators to be able also to deliver edge applications wherever you are, in whatever network, with whatever technology. >> We have that expectation on the consumer side, that it's just going to work no matter where we are, we want apps to be updated, whether I'm banking or I'm shopping for groceries, I want to make sure that they know who I am, the data's got to be there, it's got to be real time, it's got to be right, it's got to serve me personally, but it just has to work. You guys talked about some of the big challenges, but also the opportunities in terms of the future of networking, the data turning companies in the data companies. Walk us through the future of networking from Telephonica's lens, you talked about some of the big initiatives that you have by 2024. >> Yes. >> But if you had a crystal ball and you could look in there and go it looks like this for operators, what would you say? And I'd love to get your feedback too. >> Yeah, I liked how Juan Carlos talked about how the future is, I think I want to add one thing to it, to say, a lot of times the user is no longer a consumer, it's an automated thing, you know, AI think robots, so a lot of times, more and more the usage is happening by some autonomous thing and it needs to always connect. And more and more these things are extending to places where even cellular coverage doesn't exist today, so you have edge compute show up. So, and when you think about it, the things we have to solve as a community here and this is all the discussions is, number one, how you make it a fully open standard model, so everything plugs and play, more and more, there's so many pieces coming, software, hardware, from different components and the integration of all of that is probably one of the biggest challenges people want solved. You know, how it's no longer one box, you buy from one person and put it away, now you have a complex combination of hardware and software. Also the operational model is very important and that is one of the areas we're focused on at Dell, is that while the operational model works inside the data centers for certain application, for telcos, it looks different when you're out at the cell tower and you're going to have these extended temperature changes. And sometimes this may not be inside a cabinet, maybe outside and the person servicing it is not an IT technician. This is somebody that needs to know exactly how to plug it, to be able to place equipment quickly and add capacity, those are just two of the areas, the cloud, making it work like a cloud, where it's intuitive, automated and you can easily add capacity, you can, you know, get a lot of monitoring, a lot of metrics, those are some of the things that we're all solving in this community. >> Let's talk about exactly how you're achieving this, Telephonica and Dell have been working together for a couple of years, you said before we went live. Talk about, you're doing this, you talked about the challenges, the opportunities how are you solving them and why with Dell? >> Okay, well you need to go with the right partners, not to this kind of process of transforming your network into a digital platform. There are big challenges on creating the cloud infrastructure that you need to support the complex, functionality and network requires. And I think you need to have with you, companies that know about the processors, that know about the hardware, the server, that know about how to make an abstraction of that hardware layer so that you can manage that digitally and this is not something any company can do, so you need companies that are very specialized. Telecom operators are changing the way to work, we work in the past with traditionally, with network equipment vendors, now we need to start working with technology providers, hardware (indistinct) providers with cloud providers with an ecosystem that is probably wider than what we had in the past. >> Yes. >> So I come from a background, I call myself a "knuckle dragging hardware engineer" sort of guy, so I'm almost fascinated by the physical part of this. You have a network, part of that network includes towers that have transmitters, receivers, at the base of those towers and like you mentioned, they're not all necessarily in urban areas or easy to access. There's equipment there, let's say that, that tower has been there for 5 years, 10 years, in the traditional world of IT, we have this this concept of the "refresh cycle" >> Juan Carlos: Yeah. >> Where a server may have a useful life of 36 months before it's consuming more power than it should based on the technology. How do you move from, kind of a legacy more proprietary, all-inclusive stack to an open system? I mean, is this a, "Okay, we're planning for an outage for the tower and you're wheeling out old equipment and wheeling in new equipment?" >> Juan Carlos: Yeah. >> I mean that's not, that's what we say as a non-trivial exercise, it's something that isn't, it's not something that's just easy to do, but is that what progress looks like? Sort of, methodically one site at a time? >> Yeah, well, I mean, you have touched an important point. In the technology renewal cycles, we were taking an appliance and replacing that by another one. Now with the current technology, you have the couple, the hardware from the software and the hardware, you need to replace it only when you run out of processing capacity to do what you want, okay? So then we'll be there 2, 3, 4, 5 years, whatever, when you need additional capacity, you replace it, but on the software side you can make the replacement every hour, every week. And this is something that the new technologies are bringing, a flexibility for the telecom operator to introduce a new feature without having to be physically there in the place, okay, by software remotely and this is the kind of software network we want to build. >> Lisa Martin: You know- >> Yeah, I want to add to that if I can- >> Please. >> Yeah. >> I think this is one of the biggest benefits of the open model. If the stack is all integrated as one appliance, when a new technology, we all know how quickly selecon technology comes out and now we have GPU's coming out for AI more increasingly, in an appliance model it may take you two years to take advantage of some new selecon that just came out. In this new open model, as Juan Carlos was saying, you just swap out, you know, you have time to market CPUs launched, it can be put out there at the cell tower and it could double capacity instantly and we're going to need that in that world, that easily going to be AI enabled- >> Lisa Martin: Right. >> So- >> So my last question to you, we only got a minute left or so, is given everything that we've talked about, the challenges, the opportunities, what you're doing together, how would you Juan Carlos summarize how the business is benefiting from the Dell partnership and the technologies that you're enabling with this new future network? >> Well, as I said before, we will need to be able to cover all the characteristics and performance of our network. We will need the right kind of processing capacity, the right kind of hardware solutions. We know that the functionality of the network is a very demanding one, we need hardware acceleration, we need a synchronization, we need time-sensitive solutions and all these can only done by hardware, so you need a good hardware partner, that ensures that you have the processing capacity you need to be able then to run your software, you know, with the confidence that it will work and with the performance that you need. >> That confidence is key. Well it sounds like what Telephonica and Dell have achieved together has been quite successful. Congratulations on the first couple of years, sounds like it's really helping Telephonica's business move in the strategic direction that it wants. We appreciate you joining us on the program today, describing all this, thank you both so much for your time. >> Thank you very much. >> Thank you, this was fun. >> A pleasure. >> Good, our pleasure. For our guests and for Dave Nicholson, I'm Lisa Martin, you're watching theCUBE live day two from Barcelona, MWC 23. Don't go anywhere, Dave and I will be right back with our next guests. (cheerful bouncy music)

the cube presents ignite 22. brought to you by Palo Alto Networks hey guys and girls welcome back to Las Vegas it's thecube we are live at Palo Alto networks ignite 22. this is day one of two days of cube coverage Lisa Martin here with Dave vellante Dave we've had great conversations today talking with Executives the partner ecosystem is evolving it's growing at Palo Alto networks going to be digging into that next well we heard a lot of talk about you know Palo Alto you know the goal 100 billion dollar you know market cap company and to me a way and I think a critical way in which you get there is partner with the ecosystem because you can't do it alone the power of many versus the resources of one agree completely agree we've got Carl Sutherland with us SVP of North America ecosystem sales at Palo Alto networks welcome to the cube thanks so much for having me it's great being here so here we are the first full day of the conference actually started yesterday with the partner Summit give the audience a flavor of the partner Summit who was there what was talked about what's the current voice of the partner these days yeah great questions so we had a 150 Partners from around the globe representing all of our different routes to Market and for us our partner Community is expanding we work with system integrators we work with gsis we work with service providers Distributors traditional value-added resellers so it was a whole host of partners that were there it was a c-level audience and we really talked about the direction of where we're going as a company how they can continue to invest with us and have greater success long term and so from a voice of the partner standpoint what they're here to do is share with us where they want to engage more how we can enable them to be successful you talked about the Power of One Versus a community we're really looking at a segment of the marketplace right now for us to scale and hit our aspirational goals we can't do it with Palo Alto Network employees we have an employee base of 12 000 people if you take our ecosystem it's over a hundred thousand employees so if we can get them aligned and selling and motivated it's going to be a good day for all of us what so what are they telling you where do they want to spend their time where do they want to add value where are they winning yeah that's a great question so there's a transformation that's going on right now in the partner Community what's happening is a lot of Partners going that are transitioning from what would be traditional transactional Partners or resale Partners to being services-led and the Market's driving them there and what I mean by that is that customers are in a desperate dire State needing assistance figuring out and solving these very complex security problems so if there is a subset of Partners out there that have the skill set and capabilities that can come in from a consultative standpoint help them to develop the structure through deployment a full-blown management and do life cycle management that's a tremendous value I mean the numbers you hear thrown around in the industry right now is up to seven million uh security I.T jobs right now that are out there the open head count is tremendous people can't hire people fast enough all of us in the industry are going through and trying to find early in career or college graduates so we can train quickly or cross-train from other segments to get them into cyber security so if our part of the community can continue to get skilled and expand it's only going to help and the cloud is obviously where does the cloud fit in Carl because you know a lot of the partners when the clouds really start on the Steep part of the s-curve are like we have an opportunity here and by the way if we don't transition our business we could get commoditized yes so that you know that but you were talking about the transactional we can help people move to the cloud and a big part of that has got to be we can secure them in the cloud because it's a more in a lot of ways you know Cloud security is great but in a lot of ways it adds complexity what are you hearing from the party yeah so we are fortunate at Palo Alto networks when you look across the three loud largest cloud service provider from a Google AWS and Microsoft Azure we're either their number one isv or absolutely their number one security ISP so we've got a great uh relationships with them now our partners are coming along and saying how do we transact how do we add value a lot of times that value to your question is wrapping services around it to make sure it's a successful deployment because exactly what you stated the complexity is an all-time high so how do we make sure that we can solve a complex problem in a short term while increasing their security posture and that's really the goal and so where there there's sometimes complexity and mystery there's opportunity and partners can be profitable in doing that I wrote a piece once chaos is cash I have a security you know the criminals and vendors as well yes yes where there is is challenge and complexity there is great opportunity yeah talk about some of the partner program Evolution and some of the things that were announced with respect to the next wave program just yesterday yeah so at next wave um the program's been around for 12 years we constantly are looking to make enhancements and how we make those enhancements are by going out and speaking with these partners and listening to what they need so I have the honor to get to represent what their needs are and how we bring it to market for them so a couple interesting announcements that we made yesterday first of all we announced a new structural format for the program which is really going to allow our different route to markets to have a program that's fit for them because in the past when we were just traditionally a firewall company when the ecosystem just meant resale it was an easy model to have it's complex right now sometimes it's resale sometimes it's influence sometimes its services only we really need to be flexible and credible so we announced a Services only path so if you are a consulting company if you are a insurance company and you want to bring opportunities and leads to Palo Alto Network and you want to provide the services if you're not interested in the transaction you don't want to get involved in that we now have a pathway for you to support you to enable you and Kennedy to give you recognition within Palo Alto networks from an alignment standpoint so we're super excited about that uh as I know you guys speak quite a bit about the managed Services industry so it's a red hot area within Palo Alto networks one of the needs out there was that all not all managed Service Partners are created equally and so some have fantastic capabilities some have gaps we were calling it a P2P part of the partner program within managed services so our two managed Services Partners can actually work together to solve the problem that the end user has and give them a better outcome and fill each other's gaps so candidly it's been going on for a while the partnering but we've never really recognized it so we really built a program around it and now are sponsoring and supporting it versus people doing it on a sidebar so those guys were here in force yesterday yes sir right and and so obviously a lot of energy I'm sure do you see a day where they're here in force on the show floor yeah and and how do you see that evolving so they are here enforcement just right here you see a few of them I'm looking at AWS who's our you know we are their largest isv I'm looking at CDW we had them on the floor is our if not largest second largest partner globally right now and continuing to grow at a rate well they will probably be our first billion dollar partner to think about the size and scale of that relationship and where we've come from um their name CDW don't they never really thought of CDW right as a as a security firm wow what a transformation but please carry on and think about that let's talk about CDW saying think about reach that CDW has it's a 23 billion dollar organization and in a way an inside out sales model meaning there's a tremendous reach they have from their inside sales team and the relationships that they have traditionally historically they were procurement relationships in a way and I said this to the CDW team they were the easy button in the past now what they're doing is they made Seven Acquisitions over the last two years all of them Services oriented so now they're coming in as a consultative Viewpoint and solving a lot of complex problems and I see Google Cloud right here another great partner for us that we continue to invest in we have a great amount of integration and Technology integration with them and so and those are the three that I'm seeing just looking over my left shoulder right if I turn around I'll probably name five more so the majority of this room are the partners that fall within our ecosystem today fantastic so okay so what's your vision for where you want to take this ecosystem because as I said at the top I mean ecosystems are sort of the Hallmark of a I guess you're not a cloud company see I think you of you as a cloud company and so okay good so and I know you don't own your own public cloud and you know your history is you had your own data centers but yeah but you're the security Cloud yeah and so a security Cloud any Cloud needs a great ecosystem so what's your vision for the ecosystem let's go you know five plus years out sure you we start with the end in mind and what I mean by that is we always start with the end user what's the end user's needs the end user today needs flexibility with how they consume the technology they need help in how they support and deploy the technology they need guidance in how they plan out for their future and what their growth is so what we're doing is building a very diverse set of Partners in our ecosystem that all have special skills that they bring to the table so when nikesh sits up here and talks about being a 10 billion or a 20 billion or a 50 billion dollar company we absolutely cannot do it without our ecosystem and without having a very diverse ecosystem that all has different skills that can help us scale because again Palo Alto does not want to be a services company right let's work with the people who are the best at that when we think about the deloittees and accentures and the value they have within the end user base and our joint customer base what a fantastic time to to partner together and solve those boardroom challenges and that's where I really see the vision is that at the boardroom we're building out a plan that's three to five years that's going to continue to increase their security posture because we're not thinking if we're not forward thinking like that will be left behind because the Bad actors are thinking about how they find the different areas to penetrate they're getting so sophisticated the badocracy adversaries they are well funded they're motivated Grant the ransomware attack numbers in terms of the Velocity the complexity yes no longer are we going to get if it's when yeah uh big challenge for organizations Acro across I mean really across an organization regardless of Industry are you guys having any conversations with boards in the partner organization to help align the board with the executive level and really not just have security as a board level initiative but actually being able to execute a strategy yeah and you you nailed it it's not an initiative the initiative to me means there's a beginning and an end right a strategy means there's going to be a comprehensive approach how you continue to improve and we are very fortunate that a lot of our largest Partners around the globe have that position within the boards where they are the trusted advisor so what we're doing now is enabling them and giving them the skills so they can have a more comprehensive conversation around our platform approach around the challenges you know BJ I knew who was with you earlier today likes to say that the average customer he goes and sees has 50 to 70 disparate Technologies within their environment how do you manage that how do you maintain it how do you do renewals oh and by the way most likely the people who actually initially procured that aren't with you anymore they're in a different company so the need for a platform approach is there more so than ever but the decision for the platform quite often has to come from the most senior levels within the organization because again I'm going to go back to your what was your chaos line that you said chaos is Cash chaos is Cash well also chaos is job security so if you're at at the lower level within an organization that chaos and that magic gives you a little job security but that's a short term long term you really need to think about how you're protecting the environment holistically so it is a boardroom decision down that we need to have and you know that chaos the the motivation for that piece that I wrote was from the criminals standpoint right and then I was like okay but there's great opportunities for the technology industry but but I think that you know where we're headed I wonder if I get your thoughts on thoughts on this Carlos we always talk about the Board Room I think we're going now Beyond it here I am you know I'm hypersensitive about my security I got password managers two-factor authentication I don't want SMS based two-factor authentication I want my own authenticator and that's still not enough yeah I got air gaps yeah you know for my crypto you know and I'm super paranoid my point is I think the the individuals are getting much more Savvy about security why because we've all been hacked you know it's like when you lost your data in the because you weren't backed up you know that never happens anymore it's in the cloud or you know some people have multiple backups so it's it's becoming a cultural Trend beyond the board and it's because of the board lord said hey this is really important and so I think it's not only top down I think you're going to see bottom up and middle out and the exciting part for Palo Alto networks is and maybe for you as well is there any more exciting environment to talk about that's rapidly changing and constantly changing you could come back next week and our conversation is going to change as far as what we're doing we constantly need to be thinking three steps ahead of where we're going to move and be flexible and dynamic enough to change and that's what's going to keep us ahead of the economy yeah there's no segment as Dynamic I mean data is dynamic but not as fast changing as cyber I mean because of the adversary as you mentioned I mean so smart so now now they have open adversary ecosystems I mean the adversaries are building ecosystems right absolutely insane I've got peers that are bad guys yeah right right chaos is Cash what's your favorite partner story that you think really demonstrates the value of the ecosystem that Palo Alto networks has built yeah so without sharing names I'll talk about a large U.S national partner that was very uh that was founded on a networking business and partnered with a very large networking company and built that business and was successful doing that they wanted to Pivot into the security space and very early on they made a commitment to Paulo and Ulta networks to say we're going to learn we're going to invest we're going to align with your sales force and we're going to work together and right now they are our largest partner globally and they grew 70 year over year wow so think about that this is not on a small base we're talking about a half a billion dollars in Revenue growing at 70 year over year because to your point earlier it wasn't an initiative it was a strategy and they're executing on the strategy so I tell a lot of we call War Stories like that to other partners that are looking to invest from different markets it could be a large service provider that's you know trying to transform themselves into a security player and talk about the potential of what it could be in for their Marketplace and by the way I say publicly quite often Palo Alto networks will be your most profitable relationship that you have because of the total addressable Market that we're going after because of the solutions that we bring to Market and because of the opportunity within the end users right now and we're excited I want to come back to the mssp in that in its context so we've seen the rise of the mssp and particularly you know we were talking earlier I think it was with Wendy that uh no it was with CDW like 50 of the organizations in North America don't even have a sock yeah right so they need a service provider to come out so you said we you don't want to be in the services business right you're a product company right and that's from a financial standpoint that's phenomenal you're roughly 50 billion dollar market cap company let's let's call it six billion in Revenue so that's a nice Revenue multiple 8X you know and and and the Market's down so you're a 10x Revenue multiple company typically services companies are a 1x or a 2X are you seeing a change there where technology is giving these service providers operating leverage where they're able to scale whether it's because of the cloud because of the Partnerships the Eco would you call it before the the peer-to-peer ecosystem yes like the Gap fillers yes are you do you see the economics of services changing yeah from a baseline economic standpoint not looking at the valuations but let's look at it from a an opportunity to be profitable with Palo Alto networks we know if you are just doing the transaction you have a certain range of margin that you're going to make in the opportunity we know if you wrap services around it you're going to get 3x to 4X that margin we know that if it's managed services and there's life cycle management you're talking 5x to 8X that initial transaction and by the way it's recurring revenue for them so when you think about it if you just do a transaction you're only recurring revenue is a renewal that's predictable but it's not extremely profitable now we're saying the operating leverage you get is if you wrap that services and you're going to have an increased opportunity for a greater margin and it's sticky it's hard to replace a partner who's adding value to your team and A lot of times you walk in the end user you can't tell who the partner is and who the end user is because they are one team that's value yes and that's going to drive ebit yep for your partners and that's going to drive valuation you know you know I want to come back to valuation not that I'm not you can do that okay but because I was I predicted I do my prediction post every year and I predicted last year that we're going to see you know a Spate of MSS mssps I predicted you're going to see someone go public nobody's going public these days but I still think it's a great business yeah that's an untapped opportunity it's not an 8X or it's not a software marginal economics or but it's really sticky super high value yeah and I think it has you know long-term potential yeah to your point if you want to talk valuations for a second let's look at what's happened to the marketplace over the last 12 to 18 months the large majority of the non-public partners that we work with have taken on Capital from private Equity the private Equity that has come in has challenged them to go through a transformation that transformation is you we need you to be Services LED and that service is value because they believe there's going to is going to be a great greater evaluation from that end and they'll be able to scale and grow and stay ahead of the market doing that so when we have conversations when I have conversations yes I'm talking about the technology and the direction of the company but I'm also in there as a consultant saying where's the direction of your company and how do we have this great platform and how do we build it into your business and you wrap services around it and those are the conversations that CEOs want to have when I'm sitting down with our partner CEOs I bet they don't want to talk about our product being better than someone else's product they want to talk about the direction and health of their business yeah it's their business that's a business discussion business decision and they're thinking about okay what's my five-year strategic plan because they got to make bets yeah they're going to bet on a platform that they can add value to that creates that flywheel effect and they get a bet on your ecosystem as well correct oh correct absolutely good to be the leader it's good to be a leader and you know I'm sure as you've heard a few times we believe that economic headwinds are going to favor the market leaders and economic headwinds are going to favor the platform approach so we're going in more aggressive with our partner Community than ever before and there's just so much energy and excitement I feel like I keep on using that term over and over again but that's really what we walk away with last question for you is we have about 30 seconds left a lot of momentum in the partner ecosystem as you've described eloquently what's next what's next what's next yeah so when I I rolled out the strategy for what's next and what it is is a foundational platform that is going to allow flexibility for the partners and for them to decide where they want to invest and it can be in new areas it can be I went online closer with the cloud service providers it could be I want to build a managed Services business can you help us do this it could be I want to go through and I want to drive greater penetration into geographical areas we haven't been before so again we're almost acting as a consultant looking at what they're going from the direction and building a program and a platform where we can grow and work with them it's exciting it's fun it's great highly collaborative highly collaborative highly collaborative thank you for joining us on the program on the partner program the ecosystem Better Together what you guys are doing and ultimately how it benefits the end user customer we really appreciate your insights excellent thank you thank you so much appreciate it all right our pleasure for our guests and Dave vellante I'm Lisa Martin you're watching the cube the leader in live Enterprise and emerging Tech coverage [Music]

>>Hello, welcome back to the cubes coverage of Monaco, crypto summit presented by digital bits. It's a conference where a lot of the people using digital bits and the industry coming together around the future of crypto in the applicates got a great guest garage, rod cot, founder, and CEO of an innovative company. Love this co I love this company, Luke mogul, Rob, thanks for coming on the queue. Appreciate it. Oh, >>Thank you for having >>Us. Yeah. So I checked out what you guys are doing. You've got the sports metaverse angle going on with super valuable, cuz sports is super entertaining. Uh, people are engaged. There's huge fan base, huge online now, digital convergence going on with the physical, you know, you see all kinds of sports betting going on now everything's going digital. There's a whole nother consumer experience going on with sports and the game is still the same on the, on the field or so to, or the court. That's correct. Yeah. Now it's going to digital take a minute to explain what you guys are working on. >>Yeah, so yes, we are building out a sports ERs where we are bringing athletes, whether they're NBA stars, NFL stars, w N B a many of those athletes into meows giving them the ownership of the entire, um, meows commerce along with gameplay. So that's something from our perspective, this, uh, this is something that we're focused on. We're building out stadiums. Athletes can own stadiums. Athlete can create their own training centers, media hubs. Um, and imagine Lisa, Leslie for example, is building out a woman leadership sports academy, right? We have Michael Cooper building out defensive academy. So those are all the brands. We have 174 NBA w N B stars. And, um, and we are building out this, >>The brand is the brand, is the platform that's correct. That's the trend we're seeing. And it's, it's also an extension of their reach in community. So there's, they can convert their star power and athlete with owner's approval. If they probably write it on to the contracts, he, they can imagine all the complications, but they bring that online and extend that energy and brand equity yep. To fans and social network. Yeah. >>And many of these athletes are tremendous successful in their web two careers, right? Yeah. Um, some are current athletes, some are former athletes, but they have built such a brand persona where people are following them on Instagram. For example, Carlos Boozer. He has like almost 6 million followers between Twitter and Instagram and those kind of brands are looking or how do I give back to the community? How do I engage with my community and web three? And especially with our platform, we are giving that power back to the players. >>So you guys got some big names booers on there. You mentioned Carlos Boozer. You mentioned that Lisa, Leslie others among others, Michael Cooper throw back to the old Lakers, uh, magic. Johnson's kind actually here in crypto. We just saw him in the lobbies and in dinner and the other night, um, at Nobu, um, you got a lot of NBA support. Take a take, take, even explain how you're working this angle. Uh, you got some great traction, uh, momentum. Um, you got great pedigree, riot games in your career. Uh, you kind of get the world, the tech world, the media world, as it comes together. What's the secret sauce here? Is it the NBA relationship combination of the team explained >>It's really focusing on what, uh, we are building on me was focusing on players first, right? So players are literally, we call our platform as, uh, owned by the players, made for the players. Uh, and engagement is really all done through the players, right? So that's our key sauce. And when we worked out with NBA, we, we are part of the NBA BPA acceleration program for 2022 that is funded by a six Z, uh, and, and many others. Um, and our partnership with league is very critical. So it's not only partnered with player association partnered with leagues, whether it's NBA, w N B a NFL. So those are the venues. And this becomes almost a program, especially for athletes to really generate this lifetime engagement and royalty model because some of this famous athletes really want to give back to the communities. So like for example, I use Lisa Leslie a lot, but Lisa, Leslie really wants to empower women leadership, leadership, and really help, um, women in sports, for example. Right? So those are the angles that, um, that really people are excited about. >>Well, for the people watching that might not understand some of the ins and outs of sports and, and rod, your background in your team, it's interesting. The sports teams have been on the big day to train for many, many years. You look at all the stadiums. Now they've got mobile devices, they got wifi under the chairs. They use data and technology to manage the team. Mm-hmm, <affirmative> manage the stadium and venue and operations suppliers, whatnot. And then also the fans. So you, they, they got about a decade or so experience already in the digital world. This is not new to the, to the sports world. Yeah. So you guys come to the table kind of at a good time. >>Yeah. Especially the defi of the sports, right? So there's a defi of the finance, but this is the really, uh, a, a decentralization of the sports is something that there's a lot of traction. And there are many companies that are really focusing on that. Our focus obviously is players first, right? How do we give power to the players? Uh, and those are really driving the entire engagement. And also the brands >>How's the NBA feel about this because, you know, you got the NBA and you get the team, you got the owners. I mean, the democratization of the players, which I love by the way that angle kind of brings their power. Now's the new kind of balance of power. How is the NBA handling this? What's some of the conversations you've had with the, the organization. >>Yeah. So obviously there are a lot of things that, uh, people have to be careful about, right? They have existing contracts, existing, digital media rights. Um, so that's something that, uh, we have to be very tactful when we are working with NBA and NPA, uh, on what we can say, we cannot say. So that is obviously they have a lot of existing multimillion or billion dollar contracts that they cannot void with the web because the evolution of web three, >>You know, I love, uh, riffing on the notion of contract compliance when there's major structural change happening. Remember back in baseball, back in the days before the internet, the franchise rights was geographic territory. Mm-hmm <affirmative> well, if you're the New York Yankees, you're doing great. If you're Milwaukee, you're not doing too good, but then comes the internet. That's good. That's no geography. There's no boundaries. That's good. So you're gonna have stadiums have virtual Bo. So again, how do they keep up with the contracts? Yeah. I mean, this is gonna be a fundamental issue. >>That's >>Good. Good. And I think if they don't move, the players are gonna fill that void. >>That's correct. Yeah. And especially with this, this an IL deal, right. That happened for the players, uh, especially college athletes. So we are in process of onboarding 1.5 million college athletes. Uh, and those athletes are looking for not only paying for the tuition for the colleges, but also for engagement and generating this early on, uh, >>More okay. Rod, we're gonna make a prediction here in the cube, 20, 20 we're in Monaco, all the NBA, NHL, the teams they're gonna be run by player Dows. Yeah. What do you think? A very good prediction. Yeah. Very good prediction. Yeah. I mean you, I mean, that's a joke, I'm joking aside. I mean, it's kind of connecting the dots, but you know, whether that happens or not, what this means is if this continues to go down this road, that's correct. Get the players collectively could come together. Yeah. And flip the script. >>Yeah. And that's the entire decentralization, right. So it's like the web three has really disrupted this industry as you know. Um, and, and I know your community knows that too. >>Of course, course we do. We love it. >>Something from sports perspective, we are very excited. >>Well, I love it. Love talking. Let's get to the, to the weeds here on the product, under the hood, tell about the roadmap, obviously NFTs are involved. That's kind of sexy right now. I get the digital asset model on there. Uh, but there's a lot more under the coverage. You gotta have a platform, you gotta have the big data and then ultimately align into connecting other systems together. How do you view the tech roadmap and the product roadmap? What's your vision? >>Yeah. So the, the one thing that you had to be T full, uh, as a company, whether it's LUT, mogul or any other startup, is you have to be really part of the ecosystem. So the reason why we are here at Monaco is that we obviously are looking at partnership with digital bits, um, and those kind of partnership, whether it's fourth centric, centric are very critical for the ecosystem in the community to grow. Um, and that's one thing you cannot build a, another, uh, isolated metaverse right? So that's one thing. Many companies have done it, but obviously not. >>It's a wall garden doesn't work. >>Exactly. So you have to be more open platform. So one things that we did early on in our platform, we have open APIs and SDKs where not only you as an athlete can bring in your, uh, other eCommerce or web, uh, NFTs or anything you want, but you can also bring in other real estate properties. So when we are building out this metaverse, you start with real estate, then you build out obviously stadiums and arenas and academies training academies, but then athletes can bring their, uh, web commerce, right. Where it's NFT wearables shoe line. So >>Not an ecosystem on top of Luke Mo. So you're like, I'm almost like you think about a platform as a service and a cloud computing paradigm. Yeah. Look different, not decentralized, but similarly enabling others, do the heavy lifting on their behalf. Yeah. Is that right? >>So that's correct. Yes. So we are calling ourself as the sports platform as a service, right. So we want to add the word sports because we, uh, in, in many contexts, right. When you're building metaverse, you can get distracted with them, especially we are in Los Angeles. Right. >>Can I get a luxury box for the cube and some of the metaverse islands and the stadiums you're doing? >>We, we are working >>On it. We're >>Definitely working on, especially the, uh, Los Angeles, uh, stadium. Yeah. >>Well, we're looking for some hosts, anyone out there looking for some hosts, uh, for the metaverse bring your avatar. You can host the cube, bro. Thanks for coming on the cube. Really appreciate. What's the, what's next for you guys, obviously, continuing to build momentum. You got your playful, how many people on the team what's going on, give a plug for the company. What are you looking for share with the audience, some of the, some of your goals. Yeah. >>So, uh, the main thing we're looking for is really, um, from a brand perspective, if you are looking at buying properties, this would be an amazing time to buy virtual sports stadium. Um, so we are, obviously we have 175 stadiums in roadmap right now. We started with Los Angeles. Then we are in San Francisco, New York, Qatar, Dubai. So all those sports stadiums, whether they're basketball, football, soccer are all the properties. And, uh, from a community perspective, if you want to get an early access, we are all about giving back to the community. Uh, so you can buy it at a much better presale price right now. Uh, so that's one, the second thing is that if you have any innovative ideas or a player that you want to integrate into, we have an very open platform from a community engagement perspective. If you have something unique from a land sale perspective yeah. Or the NFD perspective plug, contact us at, at Raj lumo.com. >>And I'm assuming virtual team, you in LA area where where's your home. >>So, yeah, so I live in Malibu, um, and our office is in Santa Monica. We have an office in India. Uh, we have few developers also in Europe. So, uh, and then we are team of 34 people right now >>Looking to hire some folks >>We are looking for, what >>Are you, what are you looking for? >>So, uh, we are looking for a passionate sports, uh, fanatics. >>It's a lot, not hard to find. Yeah. >><laugh> who knows how to also code. Right? So from blockchain perspective, we are, uh, chain agnostic. Uh, but obviously right now we are building on polygon, but we are chain agnostic. So if you have any blockchain development experience, uh, that's something we, we are looking for. Yeah. >>RA, thanks for coming out. Luke Mo check him out. I'm John furry with the cube here in Monaco for the mono crypto summit presented by digital bits. We got all the action, a lot of great guests going on, stay with us for more coverage. Um, John furrier, thanks for watching.

>> From theCUBE studios in Palo Alto in Boston, bringing you data-driven insights from the cube and ETR, this is Breaking Analysis with Dave Vellante. >> Organizations have considerable room to improve their performance without making expensive changes to their talent, their structure, or their fundamental business model. You don't need a slew of consultants to tell you what to do. You already know. What you need is to immediately ratchet up expectations, energy, urgency, and intensity. You have to fight mediocrity every step of the way. Amp it up and the results will follow. This is the fundamental premise of a hard-hitting new book written by Frank Slootman, CEO of Snowflake, and published earlier this year. It's called "Amp It Up, Leading for Hypergrowth "by Raising Expectations, Increasing Urgency, "and Elevating Intensity." Hello and welcome to this week's Wikibon CUBE Insights, powered by ETR. At Snowflake Summit last month, I was asked to interview Frank on stage about his new book. I've read it several times. And if you haven't read it, you should. Even if you have read it, in this Breaking Analysis, we'll dig deeper into the book and share some clarifying insights and nuances directly from Slootman himself from my one-on-one conversation with him. My first question to Slootman was why do you write this book? Okay, it's kind of a common throwaway question. And how the heck did you find time to do it? It's fairly well-known that a few years ago, Slootman put up a post on LinkedIn with the title Amp It Up. It generated so much buzz and so many requests for Frank's time that he decided that the best way to efficiently scale and share his thoughts on how to create high-performing companies and organizations was to publish a book. Now, he wrote the book during the pandemic. And I joked that they must not have Netflix in Montana where he resides. In a pretty funny moment, he said that writing the book was easier than promoting it. Take a listen. >> Denise, our CMO, you know, she just made sure that this process wasn't going to. It was more work for me to promote this book with all these damn podcasts and other crap, than actually writing the book, you know. And after a while, I was like I'm not doing another podcast. >> Now, the book gives a lot of interesting background information on Slootman's career and what he learned at various companies that he led and participated in. Now, I'm not going to go into most of that today, which is why you should read the book yourself. But Slootman, he's become somewhat of a business hero to many people, myself included. Leaders like Frank, Scott McNealy, Jayshree Ullal, and my old boss, Pat McGovern at IDG, have inspired me over the years. And each has applied his or her own approach to building cultures and companies. Now, when Slootman first took over the reins at Snowflake, I published a Breaking Analysis talking about Snowflake and what we could expect from the company now that Slootman and CFO Mike Scarpelli were back together. In that post, buried toward the end, I referenced the playbook that Frank used at Data Domain and ServiceNow, two companies that I followed quite closely as an analyst, and how it would be applied at Snowflake, that playbook if you will. Frank reached out to me afterwards and said something to the effect of, "I don't use playbooks. "I am a situational leader. "Playbooks, you know, they work in football games. "But in the military, they teach you "situational leadership." Pretty interesting learning moment for me. So I asked Frank on the stage about this. Here's what he said. >> The older you get, the more experience that you have, the more you become a prisoner of your own background because you sort of think in terms of what you know as opposed to, you know, getting outside of what you know and trying to sort of look at things like a five-year-old that has never seen this before. And then how would you, you know, deal with it? And I really try to force myself into I've never seen this before and how do I think about it? Because at least they're very different, you know, interpretations. And be open-minded, just really avoid that rinse and repeat mentality. And you know, I've brought people in from who have worked with me before. Some of them come with me from company to company. And they were falling prey to, you know, rinse and repeat. I would just literally go like that's not what we want. >> So think about that for a moment. I mean, imagine coming in to lead a new company and forcing yourself and your people to forget what they know that works and has worked in the past, put that aside and assess the current situation with an open mind, essentially start over. Now, that doesn't mean you don't apply what has worked in the past. Slootman talked to me about bringing back Scarpelli and the synergistic relationship that they have and how they build cultures and the no BS and hard truth mentality they bring to companies. But he bristles when people ask him, "What type of CEO are you?" He says, "Do we have to put a label on it? "It really depends on the situation." Now, one of the other really hard-hitting parts of the book was the way Frank deals with who to keep and who to let go. He uses the Volkswagen tagline of drivers wanted. He says in his book, in companies there are passengers and there are drivers, and we want drivers. He said, "You have to figure out really quickly "who the drivers are and basically throw the wrong people "off the bus, keep the right people, bring in new people "that fit the culture and put them "in the right seats on the bus." Now, these are not easy decisions to make. But as it pertains to getting rid of people, I'm reminded of the movie "Moneyball." Art Howe, the manager of the Oakland As, he refused to play Scott Hatteberg at first base. So the GM, Billy Bean played by Brad Pitt says to Peter Brand who was played by Jonah Hill, "You have to fire Carlos Pena." Don't learn how to fire people. Billy Bean says, "Just keep it quick. "Tell him he's been traded and that's it." So I asked Frank, "Okay, I get it. "Like the movie, when you have the wrong person "on the bus, you just have to make the decision, "be straightforward, and do it." But I asked him, "What if you're on the fence? "What if you're not completely sure if this person "is a driver or a passenger, if he or she "should be on the bus or not on the bus? "How do you handle that?" Listen to what he said. >> I have a very simple way to break ties. And when there's doubt, there's no doubt, okay? >> When there's doubt, there's no doubt. Slootman's philosophy is you have to be emphatic and have high conviction. You know, back to the baseball analogy, if you're thinking about taking the pitcher out of the game, take 'em out. Confrontation is the single hardest thing in business according to Slootman but you have to be intellectually honest and do what's best for the organization, period. Okay, so wow, that may sound harsh but that's how Slootman approaches it, very Belichickian if you will. But how can you amp it up on a daily basis? What's the approach that Slootman takes? We got into this conversation with a discussion about MBOs, management by objective. Slootman in his book says he's killed MBOs at every company he's led. And I asked him to explain why. His rationale was that individual MBOs invariably end up in a discussion about relief of the MBO if the person is not hitting his or her targets. And that detracts from the organizational alignment. He said at Snowflake everyone gets paid the same way, from the execs on down. It's a key way he creates focus and energy in an organization, by creating alignment, urgency, and putting more resources into the most important things. This is especially hard, Slootman says, as the organization gets bigger. But if you do approach it this way, everything gets easier. The cadence changes, the tempo accelerates, and it works. Now, and to emphasize that point, he said the following. Play the clip. >> Every meeting that you have, every email, every encounter in the hallway, whatever it is, is an opportunity to amp things up. That's why I use that title. But do you take that opportunity? >> And according to Slootman, if you don't take that opportunity, if you're not in the moment, amping it up, then you're thinking about your golf game or the tennis match that's going on this weekend or being out on your boat. And to the point, this approach is not for everyone. You're either built for it or you're not. But if you can bring people into the organization that can handle this type of dynamic, it creates energy. It becomes fun. Everything moves faster. The conversations are exciting. They're inspiring. And it becomes addictive. Now let's talk about priorities. I said to Frank that for me anyway, his book was an uncomfortable read. And he was somewhat surprised by that. "Really," he said. I said, "Yeah. "I mean, it was an easy read but uncomfortable "because over my career, I've managed thousands of people, "not tens of thousands but thousands, "enough to have to take this stuff very seriously." And I found myself throughout the book, oh, you know, on the one hand saying to myself, "Oh, I got that right, good job, Dave." And then other times, I was thinking to myself, "Oh wow, I probably need to rethink that. "I need to amp it up on that front." And the point is to Frank's leadership philosophy, there's no one correct way to approach all situations. You have to figure it out for yourself. But the one thing in the book that I found the hardest was Slootman challenged the reader. If you had to drop everything and focus on one thing, just one thing, for the rest of the year, what would that one thing be? Think about that for a moment. Were you able to come up with that one thing? What would happen to all the other things on your priority list? Are they all necessary? If so, how would you delegate those? Do you have someone in your organization who can take those off your plate? What would happen if you only focused on that one thing? These are hard questions. But Slootman really forces you to think about them and do that mental exercise. Look at Frank's body language in this screenshot. Imagine going into a management meeting with Frank and being prepared to share all the things you're working on that you're so proud of and all the priorities you have for the coming year. Listen to Frank in this clip and tell me it doesn't really make you think. >> I've been in, you know, on other boards and stuff. And I got a PowerPoint back from the CEO and there's like 15 things. They're our priorities for the year. I'm like you got 15, you got none, right? It's like you just can't decide, you know, what's important. So I'll tell you everything because I just can't figure out. And the thing is it's very hard to just say one thing. But it's really the mental exercise that matters. >> Going through that mental exercise is really important according to Slootman. Let's have a conversation about what really matters at this point in time. Why does it need to happen? And does it take priority over other things? Slootman says you have to pull apart the hairball and drive extraordinary clarity. You could be wrong, he says. And he admits he's been wrong on many things before. He, like everyone, is fearful of being wrong. But if you don't have the conversation according to Slootman, you're already defeated. And one of the most important things Slootman emphasizes in the book is execution. He said that's one of the reasons he wrote "Amp It Up." In our discussion, he referenced Pat Gelsinger, his former boss, who bought Data Domain when he was working for Joe Tucci at EMC. Listen to Frank describe the interaction with Gelsinger. >> Well, one of my prior bosses, you know, Pat Gelsinger, when they acquired Data Domain through EMC, Pat was CEO of Intel. And he quoted Andy Grove as saying, 'cause he was Intel for a long time when he was younger man. And he said no strategy is better than its execution, which if I find one of the most brilliant things. >> Now, before you go changing your strategy, says Slootman, you have to eliminate execution as a potential point of failure. All too often, he says, Silicon Valley wants to change strategy without really understanding whether the execution is right. All too often companies don't consider that maybe the product isn't that great. They will frequently, for example, make a change to sales leadership without questioning whether or not there's a product fit. According to Slootman, you have to drive hardcore intellectual honesty. And as uncomfortable as that may be, it's incredibly important and powerful. Okay, one of the other contrarian points in the book was whether or not to have a customer success department. Slootman says this became really fashionable in Silicon Valley with the SaaS craze. Everyone was following and pattern matching the lead of salesforce.com. He says he's eliminated the customer service department at every company he's led which had a customer success department. Listen to Frank Slootman in his own words talk about the customer success department. >> I view the whole company as a customer success function. Okay, I'm customer success, you know. I said it in my presentation yesterday. We're a customer-first organization. I don't need a department. >> Now, he went on to say that sales owns the commercial relationship with the customer. Engineering owns the technical relationship. And oh, by the way, he always puts support inside of the engineering department because engineering has to back up support. And rather than having a separate department for customer success, he focuses on making sure that the existing departments are functioning properly. Slootman also has always been big on net promoter score, NPS. And Snowflake's is very high at 72. And according to Slootman, it's not just the product. It's the people that drive that type of loyalty. Now, Slootman stresses amping up the big things and even the little things too. He told a story about someone who came into his office to ask his opinion about a tee shirt. And he turned it around on her and said, "Well, what do you think?" And she said, "Well, it's okay." So Frank made the point by flipping the situation. Why are you coming to me with something that's just okay? If we're going to do something, let's do it. Let's do it all out. Let's do it right and get excited about it, not just check the box and get something off your desk. Amp it up, all aspects of our business. Listen to Slootman talk about Steve Jobs and the relevance of demanding excellence and shunning mediocrity. >> He was incredibly intolerant of anything that he didn't think of as great. You know, he was immediately done with it and with the person. You know, I'm not that aggressive, you know, in that way. I'm a little bit nicer, you know, about it. But I still, you know, I don't want to give into expediency and mediocrity. I just don't, I'm just going to fight it, you know, every step of the way. >> Now, that story was about a little thing like some swag. But Slootman talked about some big things too. And one of the major ways Snowflake was making big, sweeping changes to amp up its business was reorganizing its go-to-market around industries like financial services, media, and healthcare. Here's some ETR data that shows Snowflake's net score or spending momentum for key industry segments over time. The red dotted line at 40% is an indicator of highly elevated spending momentum. And you can see for the key areas shown, Snowflake is well above that level. And we cut this data where responses were greater, the response numbers were greater than 15. So not huge ends but large enough to have meaning. Most were in the 20s. Now, it's relatively uncommon to see a company that's having the success of Snowflake make this kind of non-trivial change in the middle of steep S-curve growth. Why did they make this move? Well, I think it's because Snowflake realizes that its data cloud is going to increasingly have industry diversity and unique value by industry, that ecosystems and data marketplaces are forming around industries. So the more industry affinity Snowflake can create, the stronger its moat will be. It also aligns with how the largest and most prominent global system integrators, global SIs, go to market. This is important because as companies are transforming, they are radically changing their data architecture, how they think about data, how they approach data as a competitive advantage, and they're looking at data as specifically a monetization opportunity. So having industry expertise and knowledge and aligning with those customer objectives is going to serve Snowflake and its ecosystems well in my view. Slootman even said he joined the board of Instacart not because he needed another board seat but because he wanted to get out of his comfort zone and expose himself to other industries as a way to learn. So look, we're just barely scratching the surface of Slootman's book and I've pulled some highlights from our conversation. There's so much more that I can share just even from our conversation. And I will as the opportunity arises. But for now, I'll just give you the kind of bumper sticker of "Amp It Up." Raise your standards by taking every opportunity, every interaction, to increase your intensity. Get your people aligned and moving in the same direction. If it's the wrong direction, figure it out and course correct quickly. Prioritize and sharpen your focus on things that will really make a difference. If you do these things and increase the urgency in your organization, you'll naturally pick up the pace and accelerate your company. Do these things and you'll be able to transform, better identify adjacent opportunities and go attack them, and create a lasting and meaningful experience for your employees, customers, and partners. Okay, that's it for today. Thanks for watching. And thank you to Alex Myerson who's on production and he manages the podcast for Breaking Analysis. Kristin Martin and Cheryl Knight help get the word out on social and in our newsletters. And Rob Hove is our EIC over at Silicon Angle who does some wonderful and tremendous editing. Thank you all. Remember, all these episodes are available as podcasts. Wherever you listen, just search Breaking Analysis podcast. I publish each week on wikibon.com and siliconangle.com. And you can email me at david.vellante@siliconangle.com or DM me @dvellante or comment on my LinkedIn posts. And please do check out etr.ai for the best survey data in enterprise tech. This is Dave Vellante for theCUBE Insights, powered by ETR. Thanks for watching. Be well. And we'll see you next time on Breaking Analysis. (upbeat music)

(upbeat music) >> Hey everyone. Welcome back to theCUBE's live coverage of Women in Data Science, WiDS 2022, coming to live from Stanford University. I'm Lisa Martin. My next guest is here. Nandi Leslie, Doctor Nandi Leslie, Senior Engineering Fellow at Raytheon Technologies. Nandi, it's great to have you on the program. >> Oh it's my pleasure, thank you. >> This is your first WiDS you were saying before we went live. >> That's right. >> What's your take so far? >> I'm absolutely loving it. I love the comradery and the community of women in data science. You know, what more can you say? It's amazing. >> It is. It's amazing what they built since 2015, that this is now reaching 100,000 people 200 online event. It's a hybrid event. Of course, here we are in person, and the online event going on, but it's always an inspiring, energy-filled experience in my experience of WiDS. >> I'm thoroughly impressed at what the organizers have been able to accomplish. And it's amazing, that you know, you've been involved from the beginning. >> Yeah, yeah. Talk to me, so you're Senior Engineering Fellow at Raytheon. Talk to me a little bit about your role there and what you're doing. >> Well, my role is really to think about our customer's most challenging problems, primarily at the intersection of data science, and you know, the intersectional fields of applied mathematics, machine learning, cybersecurity. And then we have a plethora of government clients and commercial clients. And so what their needs are beyond those sub-fields as well, I address. >> And your background is mathematics. >> Yes. >> Have you always been a math fan? >> I have, I actually have loved math for many, many years. My dad is a mathematician, and he introduced me to, you know mathematical research and the sciences at a very early age. And so, yeah, I went on, I studied in a math degree at Howard undergrad, and then I went on to do my PhD at Princeton in applied math. And later did a postdoc in the math department at University of Maryland. >> And how long have you been with Raytheon? >> I've been with Raytheon about six years. Yeah, and before Raytheon, I worked at a small to midsize defense company, defense contracting company in the DC area, systems planning and analysis. And then prior to that, I taught in a math department where I also did my postdoc, at University of Maryland College Park. >> You have a really interesting background. I was doing some reading on you, and you have worked with the Navy. You've worked with very interesting organizations. Talk to the audience a little bit about your diverse background. >> Awesome yeah, I've worked with the Navy on submarine force security, and submarine tracking, and localization, sensor performance. Also with the Army and the Army Research Laboratory during research at the intersection of machine learning and cyber security. Also looking at game theoretic and graph theoretic approaches to understand network resilience and robustness. I've also supported Department of Homeland Security, and other government agencies, other governments, NATO. Yeah, so I've really been excited by the diverse problems that our various customers have you know, brought to us. >> Well, you get such great experience when you are able to work in different industries and different fields. And that really just really probably helps you have such a much diverse kind of diversity of thought with what you're doing even now with Raytheon. >> Yeah, it definitely does help me build like a portfolio of topics that I can address. And then when new problems emerge, then I can pull from a toolbox of capabilities. And, you know, the solutions that have previously been developed to address those wide array of problems, but then also innovate new solutions based on those experiences. So I've been really blessed to have those experiences. >> Talk to me about one of the things I heard this morning in the session I was able to attend before we came to set was about mentors and sponsors. And, you know, I actually didn't know the difference between that until a few years ago. But it's so important. Talk to me about some of the mentors you've had along the way that really helped you find your voice in research and development. >> Definitely, I mean, beyond just the mentorship of my my family and my parents, I've had amazing opportunities to meet with wonderful people, who've helped me navigate my career. One in particular, I can think of as and I'll name a number of folks, but Dr. Carlos Castillo-Chavez was one of my earlier mentors. I was an undergrad at Howard University. He encouraged me to apply to his summer research program in mathematical and theoretical biology, which was then at Cornell. And, you know, he just really developed an enthusiasm with me for applied mathematics. And for how it can be, mathematics that is, can be applied to epidemiological and theoretical immunological problems. And then I had an amazing mentor in my PhD advisor, Dr. Simon Levin at Princeton, who just continued to inspire me, in how to leverage mathematical approaches and computational thinking for ecological conservation problems. And then since then, I've had amazing mentors, you know through just a variety of people that I've met, through customers, who've inspired me to write these papers that you mentioned in the beginning. >> Yeah, you've written 55 different publications so far. 55 and counting I'm sure, right? >> Well, I hope so. I hope to continue to contribute to the conversation and the community, you know, within research, and specifically research that is computationally driven. That really is applicable to problems that we face, whether it's cyber security, or machine learning problems, or others in data science. >> What are some of the things, you're giving a a tech vision talk this afternoon. Talk to me a little bit about that, and maybe the top three takeaways you want the audience to leave with. >> Yeah, so my talk is entitled "Unsupervised Learning for Network Security, or Network Intrusion Detection" I believe. And essentially three key areas I want to convey are the following. That unsupervised learning, that is the mathematical and statistical approach, which tries to derive patterns from unlabeled data is a powerful one. And one can still innovate new algorithms in this area. Secondly, that network security, and specifically, anomaly detection, and anomaly-based methods can be really useful to discerning and ensuring, that there is information confidentiality, availability, and integrity in our data >> A CIA triad. >> There you go, you know. And so in addition to that, you know there is this wealth of data that's out there. It's coming at us quickly. You know, there are millions of packets to represent communications. And that data has, it's mixed, in terms of there's categorical or qualitative data, text data, along with numerical data. And it is streaming, right. And so we need methods that are efficient, and that are capable of being deployed real time, in order to detect these anomalies, which we hope are representative of malicious activities, and so that we can therefore alert on them and thwart them. >> It's so interesting that, you know, the amount of data that's being generated and collected is growing exponentially. There's also, you know, some concerning challenges, not just with respect to data that's reinforcing social biases, but also with cyber warfare. I mean, that's a huge challenge right now. We've seen from a cybersecurity perspective in the last couple of years during the pandemic, a massive explosion in anomalies, and in social engineering. And companies in every industry have to be super vigilant, and help the people understand how to interact with it, right. There's a human component. >> Oh, for sure. There's a huge human component. You know, there are these phishing attacks that are really a huge source of the vulnerability that corporations, governments, and universities face. And so to be able to close that gap and the understanding that each individual plays in the vulnerability of a network is key. And then also seeing the link between the network activities or the cyber realm, and physical systems, right. And so, you know, especially in cyber warfare as a remote cyber attack, unauthorized network activities can have real implications for physical systems. They can, you know, stop a vehicle from running properly in an autonomous vehicle. They can impact a SCADA system that's, you know there to provide HVAC for example. And much more grievous implications. And so, you know, definitely there's the human component. >> Yes, and humans being so vulnerable to those social engineering that goes on in those phishing attacks. And we've seen them get more and more personal, which is challenging. You talking about, you know, sensitive data, personally identifiable data, using that against someone in cyber warfare is a huge challenge. >> Oh yeah, certainly. And it's one that computational thinking and mathematics can be leveraged to better understand and to predict those patterns. And that's a very rich area for innovation. >> What would you say is the power of computational thinking in the industry? >> In industry at-large? >> At large. >> Yes, I think that it is such a benefit to, you know, a burgeoning scientist, if they want to get into industry. There's so many opportunities, because computational thinking is needed. We need to be more objective, and it provides that objectivity, and it's so needed right now. Especially with the emergence of data, and you know, across industries. So there are so many opportunities for data scientists, whether it's in aerospace and defense, like Raytheon or in the health industry. And we saw with the pandemic, the utility of mathematical modeling. There are just so many opportunities. >> Yeah, there's a lot of opportunities, and that's one of the themes I think, of WiDS, is just the opportunities, not just in data science, and for women. And there's obviously even high school girls that are here, which is so nice to see those young, fresh faces, but opportunities to build your own network and your own personal board of directors, your mentors, your sponsors. There's tremendous opportunity in data science, and it's really all encompassing, at least from my seat. >> Oh yeah, no I completely agree with that. >> What are some of the things that you've heard at this WiDS event that inspire you going, we're going in the right direction. If we think about International Women's Day tomorrow, "Breaking the Bias" is the theme, do you think we're on our way to breaking that bias? >> Definitely, you know, there was a panel today talking about the bias in data, and in a variety of fields, and how we are, you know discovering that bias, and creating solutions to address it. So there was that panel. There was another talk by a speaker from Pinterest, who had presented some solutions that her, and her team had derived to address bias there, in you know, image recognition and search. And so I think that we've realized this bias, and, you know, in AI ethics, not only in these topics that I've mentioned, but also in the implications for like getting a loan, so economic implications, as well. And so we're realizing those issues and bias now in AI, and we're addressing them. So I definitely am optimistic. I feel encouraged by the talks today at WiDS that you know, not only are we recognizing the issues, but we're creating solutions >> Right taking steps to remediate those, so that ultimately going forward. You know, we know it's not possible to have unbiased data. That's not humanly possible, or probably mathematically possible. But the steps that they're taking, they're going in the right direction. And a lot of it starts with awareness. >> Exactly. >> Of understanding there is bias in this data, regardless. All the people that are interacting with it, and touching it, and transforming it, and cleaning it, for example, that's all influencing the veracity of it. >> Oh, for sure. Exactly, you know, and I think that there are for sure solutions are being discussed here, papers written by some of the speakers here, that are driving the solutions to the mitigation of this bias and data problem. So I agree a hundred percent with you, that awareness is you know, half the battle, if not more. And then, you know, that drives creation of solutions >> And that's what we need the creation of solutions. Nandi, thank you so much for joining me today. It was a pleasure talking with you about what you're doing with Raytheon, what you've done and your path with mathematics, and what excites you about data science going forward. We appreciate your insights. >> Thank you so much. It was my pleasure. >> Good, for Nandi Leslie, I'm Lisa Martin. You're watching theCUBE's coverage of Women in Data Science 2022. Stick around, I'll be right back with my next guest. (upbeat flowing music)

[Music] in the 2010s organizations became keenly aware that data would become the key ingredient in driving competitive advantage differentiation and growth but to this day putting data to work remains a difficult challenge for many if not most organizations now as the cloud matures it has become a game changer for data practitioners by making cheap storage and massive processing power readily accessible we've also seen better tooling in the form of data workflows streaming machine intelligence ai developer tools security observability automation new databases and the like these innovations they accelerate data proficiency but at the same time they had complexity for practitioners data lakes data hubs data warehouses data marts data fabrics data meshes data catalogs data oceans are forming they're evolving and exploding onto the scene so in an effort to bring perspective to the sea of optionality we've brought together the brightest minds in the data analyst community to discuss how data management is morphing and what practitioners should expect in 2022 and beyond hello everyone my name is dave vellante with the cube and i'd like to welcome you to a special cube presentation analyst predictions 2022 the future of data management we've gathered six of the best analysts in data and data management who are going to present and discuss their top predictions and trends for 2022 in the first half of this decade let me introduce our six power panelists sanjeev mohan is former gartner analyst and principal at sanjamo tony bear is principal at db insight carl olufsen is well-known research vice president with idc dave meninger is senior vice president and research director at ventana research brad shimon chief analyst at ai platforms analytics and data management at omnia and doug henschen vice president and principal analyst at constellation research gentlemen welcome to the program and thanks for coming on thecube today great to be here thank you all right here's the format we're going to use i as moderator are going to call on each analyst separately who then will deliver their prediction or mega trend and then in the interest of time management and pace two analysts will have the opportunity to comment if we have more time we'll elongate it but let's get started right away sanjeev mohan please kick it off you want to talk about governance go ahead sir thank you dave i i believe that data governance which we've been talking about for many years is now not only going to be mainstream it's going to be table stakes and all the things that you mentioned you know with data oceans data lakes lake houses data fabric meshes the common glue is metadata if we don't understand what data we have and we are governing it there is no way we can manage it so we saw informatica when public last year after a hiatus of six years i've i'm predicting that this year we see some more companies go public uh my bet is on colibra most likely and maybe alation we'll see go public this year we we i'm also predicting that the scope of data governance is going to expand beyond just data it's not just data and reports we are going to see more transformations like spark jaws python even airflow we're going to see more of streaming data so from kafka schema registry for example we will see ai models become part of this whole governance suite so the governance suite is going to be very comprehensive very detailed lineage impact analysis and then even expand into data quality we already seen that happen with some of the tools where they are buying these smaller companies and bringing in data quality monitoring and integrating it with metadata management data catalogs also data access governance so these so what we are going to see is that once the data governance platforms become the key entry point into these modern architectures i'm predicting that the usage the number of users of a data catalog is going to exceed that of a bi tool that will take time and we already seen that that trajectory right now if you look at bi tools i would say there are 100 users to a bi tool to one data catalog and i i see that evening out over a period of time and at some point data catalogs will really become you know the main way for us to access data data catalog will help us visualize data but if we want to do more in-depth analysis it'll be the jumping-off point into the bi tool the data science tool and and that is that is the journey i see for the data governance products excellent thank you some comments maybe maybe doug a lot a lot of things to weigh in on there maybe you could comment yeah sanjeev i think you're spot on a lot of the trends uh the one disagreement i think it's it's really still far from mainstream as you say we've been talking about this for years it's like god motherhood apple pie everyone agrees it's important but too few organizations are really practicing good governance because it's hard and because the incentives have been lacking i think one thing that deserves uh mention in this context is uh esg mandates and guidelines these are environmental social and governance regs and guidelines we've seen the environmental rags and guidelines imposed in industries particularly the carbon intensive industries we've seen the social mandates particularly diversity imposed on suppliers by companies that are leading on this topic we've seen governance guidelines now being imposed by banks and investors so these esgs are presenting new carrots and sticks and it's going to demand more solid data it's going to demand more detailed reporting and solid reporting tighter governance but we're still far from mainstream adoption we have a lot of uh you know best of breed niche players in the space i think the signs that it's going to be more mainstream are starting with things like azure purview google dataplex the big cloud platform uh players seem to be uh upping the ante and and addressing starting to address governance excellent thank you doug brad i wonder if you could chime in as well yeah i would love to be a believer in data catalogs um but uh to doug's point i think that it's going to take some more pressure for for that to happen i recall metadata being something every enterprise thought they were going to get under control when we were working on service oriented architecture back in the 90s and that didn't happen quite the way we we anticipated and and uh to sanjeev's point it's because it is really complex and really difficult to do my hope is that you know we won't sort of uh how do we put this fade out into this nebulous nebula of uh domain catalogs that are specific to individual use cases like purview for getting data quality right or like data governance and cyber security and instead we have some tooling that can actually be adaptive to gather metadata to create something i know is important to you sanjeev and that is this idea of observability if you can get enough metadata without moving your data around but understanding the entirety of a system that's running on this data you can do a lot to help with with the governance that doug is talking about so so i just want to add that you know data governance like many other initiatives did not succeed even ai went into an ai window but that's a different topic but a lot of these things did not succeed because to your point the incentives were not there i i remember when starbucks oxley had come into the scene if if a bank did not do service obviously they were very happy to a million dollar fine that was like you know pocket change for them instead of doing the right thing but i think the stakes are much higher now with gdpr uh the floodgates open now you know california you know has ccpa but even ccpa is being outdated with cpra which is much more gdpr like so we are very rapidly entering a space where every pretty much every major country in the world is coming up with its own uh compliance regulatory requirements data residence is becoming really important and and i i think we are going to reach a stage where uh it won't be optional anymore so whether we like it or not and i think the reason data catalogs were not successful in the past is because we did not have the right focus on adoption we were focused on features and these features were disconnected very hard for business to stop these are built by it people for it departments to to take a look at technical metadata not business metadata today the tables have turned cdo's are driving this uh initiative uh regulatory compliances are beating down hard so i think the time might be right yeah so guys we have to move on here and uh but there's some some real meat on the bone here sanjeev i like the fact that you late you called out calibra and alation so we can look back a year from now and say okay he made the call he stuck it and then the ratio of bi tools the data catalogs that's another sort of measurement that we can we can take even though some skepticism there that's something that we can watch and i wonder if someday if we'll have more metadata than data but i want to move to tony baer you want to talk about data mesh and speaking you know coming off of governance i mean wow you know the whole concept of data mesh is decentralized data and then governance becomes you know a nightmare there but take it away tony we'll put it this way um data mesh you know the the idea at least is proposed by thoughtworks um you know basically was unleashed a couple years ago and the press has been almost uniformly almost uncritical um a good reason for that is for all the problems that basically that sanjeev and doug and brad were just you know we're just speaking about which is that we have all this data out there and we don't know what to do about it um now that's not a new problem that was a problem we had enterprise data warehouses it was a problem when we had our hadoop data clusters it's even more of a problem now the data's out in the cloud where the data is not only your data like is not only s3 it's all over the place and it's also including streaming which i know we'll be talking about later so the data mesh was a response to that the idea of that we need to debate you know who are the folks that really know best about governance is the domain experts so it was basically data mesh was an architectural pattern and a process my prediction for this year is that data mesh is going to hit cold hard reality because if you if you do a google search um basically the the published work the articles and databases have been largely you know pretty uncritical um so far you know that you know basically learning is basically being a very revolutionary new idea i don't think it's that revolutionary because we've talked about ideas like this brad and i you and i met years ago when we were talking about so and decentralizing all of us was at the application level now we're talking about at the data level and now we have microservices so there's this thought of oh if we manage if we're apps in cloud native through microservices why don't we think of data in the same way um my sense this year is that you know this and this has been a very active search if you look at google search trends is that now companies are going to you know enterprises are going to look at this seriously and as they look at seriously it's going to attract its first real hard scrutiny it's going to attract its first backlash that's not necessarily a bad thing it means that it's being taken seriously um the reason why i think that that uh that it will you'll start to see basically the cold hard light of day shine on data mesh is that it's still a work in progress you know this idea is basically a couple years old and there's still some pretty major gaps um the biggest gap is in is in the area of federated governance now federated governance itself is not a new issue uh federated governance position we're trying to figure out like how can we basically strike the balance between getting let's say you know between basically consistent enterprise policy consistent enterprise governance but yet the groups that understand the data know how to basically you know that you know how do we basically sort of balance the two there's a huge there's a huge gap there in practice and knowledge um also to a lesser extent there's a technology gap which is basically in the self-service technologies that will help teams essentially govern data you know basically through the full life cycle from developed from selecting the data from you know building the other pipelines from determining your access control determining looking at quality looking at basically whether data is fresh or whether or not it's trending of course so my predictions is that it will really receive the first harsh scrutiny this year you are going to see some organization enterprises declare premature victory when they've uh when they build some federated query implementations you're going to see vendors start to data mesh wash their products anybody in the data management space they're going to say that whether it's basically a pipelining tool whether it's basically elt whether it's a catalog um or confederated query tool they're all going to be like you know basically promoting the fact of how they support this hopefully nobody is going to call themselves a data mesh tool because data mesh is not a technology we're going to see one other thing come out of this and this harks back to the metadata that sanji was talking about and the catalogs that he was talking about which is that there's going to be a new focus on every renewed focus on metadata and i think that's going to spur interest in data fabrics now data fabrics are pretty vaguely defined but if we just take the most elemental definition which is a common metadata back plane i think that if anybody is going to get serious about data mesh they need to look at a data fabric because we all at the end of the day need to speak you know need to read from the same sheet of music so thank you tony dave dave meninger i mean one of the things that people like about data mesh is it pretty crisply articulates some of the flaws in today's organizational approaches to data what are your thoughts on this well i think we have to start by defining data mesh right the the term is already getting corrupted right tony said it's going to see the cold hard uh light of day and there's a problem right now that there are a number of overlapping terms that are similar but not identical so we've got data virtualization data fabric excuse me for a second sorry about that data virtualization data fabric uh uh data federation right uh so i i think that it's not really clear what each vendor means by these terms i see data mesh and data fabric becoming quite popular i've i've interpreted data mesh as referring primarily to the governance aspects as originally you know intended and specified but that's not the way i see vendors using i see vendors using it much more to mean data fabric and data virtualization so i'm going to comment on the group of those things i think the group of those things is going to happen they're going to happen they're going to become more robust our research suggests that a quarter of organizations are already using virtualized access to their data lakes and another half so a total of three quarters will eventually be accessing their data lakes using some sort of virtualized access again whether you define it as mesh or fabric or virtualization isn't really the point here but this notion that there are different elements of data metadata and governance within an organization that all need to be managed collectively the interesting thing is when you look at the satisfaction rates of those organizations using virtualization versus those that are not it's almost double 68 of organizations i'm i'm sorry um 79 of organizations that were using virtualized access express satisfaction with their access to the data lake only 39 expressed satisfaction if they weren't using virtualized access so thank you uh dave uh sanjeev we just got about a couple minutes on this topic but i know you're speaking or maybe you've spoken already on a panel with jamal dagani who sort of invented the concept governance obviously is a big sticking point but what are your thoughts on this you are mute so my message to your mark and uh and to the community is uh as opposed to what dave said let's not define it we spent the whole year defining it there are four principles domain product data infrastructure and governance let's take it to the next level i get a lot of questions on what is the difference between data fabric and data mesh and i'm like i can compare the two because data mesh is a business concept data fabric is a data integration pattern how do you define how do you compare the two you have to bring data mesh level down so to tony's point i'm on a warp path in 2022 to take it down to what does a data product look like how do we handle shared data across domains and govern it and i think we are going to see more of that in 2022 is operationalization of data mesh i think we could have a whole hour on this topic couldn't we uh maybe we should do that uh but let's go to let's move to carl said carl your database guy you've been around that that block for a while now you want to talk about graph databases bring it on oh yeah okay thanks so i regard graph database as basically the next truly revolutionary database management technology i'm looking forward to for the graph database market which of course we haven't defined yet so obviously i have a little wiggle room in what i'm about to say but that this market will grow by about 600 percent over the next 10 years now 10 years is a long time but over the next five years we expect to see gradual growth as people start to learn how to use it problem isn't that it's used the problem is not that it's not useful is that people don't know how to use it so let me explain before i go any further what a graph database is because some of the folks on the call may not may not know what it is a graph database organizes data according to a mathematical structure called a graph a graph has elements called nodes and edges so a data element drops into a node the nodes are connected by edges the edges connect one node to another node combinations of edges create structures that you can analyze to determine how things are related in some cases the nodes and edges can have properties attached to them which add additional informative material that makes it richer that's called a property graph okay there are two principal use cases for graph databases there's there's semantic proper graphs which are used to break down human language text uh into the semantic structures then you can search it organize it and and and answer complicated questions a lot of ai is aimed at semantic graphs another kind is the property graph that i just mentioned which has a dazzling number of use cases i want to just point out is as i talk about this people are probably wondering well we have relational databases isn't that good enough okay so a relational database defines it uses um it supports what i call definitional relationships that means you define the relationships in a fixed structure the database drops into that structure there's a value foreign key value that relates one table to another and that value is fixed you don't change it if you change it the database becomes unstable it's not clear what you're looking at in a graph database the system is designed to handle change so that it can reflect the true state of the things that it's being used to track so um let me just give you some examples of use cases for this um they include uh entity resolution data lineage uh um social media analysis customer 360 fraud prevention there's cyber security there's strong supply chain is a big one actually there's explainable ai and this is going to become important too because a lot of people are adopting ai but they want a system after the fact to say how did the ai system come to that conclusion how did it make that recommendation right now we don't have really good ways of tracking that okay machine machine learning in general um social network i already mentioned that and then we've got oh gosh we've got data governance data compliance risk management we've got recommendation we've got personalization anti-money money laundering that's another big one identity and access management network and i.t operations is already becoming a key one where you actually have mapped out your operation your your you know whatever it is your data center and you you can track what's going on as things happen there root cause analysis fraud detection is a huge one a number of major credit card companies use graph databases for fraud detection risk analysis tracking and tracing churn analysis next best action what-if analysis impact analysis entity resolution and i would add one other thing or just a few other things to this list metadata management so sanjay here you go this is your engine okay because i was in metadata management for quite a while in my past life and one of the things i found was that none of the data management technologies that were available to us could efficiently handle metadata because of the kinds of structures that result from it but grass can okay grafts can do things like say this term in this context means this but in that context it means that okay things like that and in fact uh logistics management supply chain it also because it handles recursive relationships by recursive relationships i mean objects that own other objects that are of the same type you can do things like bill materials you know so like parts explosion you can do an hr analysis who reports to whom how many levels up the chain and that kind of thing you can do that with relational databases but yes it takes a lot of programming in fact you can do almost any of these things with relational databases but the problem is you have to program it it's not it's not supported in the database and whenever you have to program something that means you can't trace it you can't define it you can't publish it in terms of its functionality and it's really really hard to maintain over time so carl thank you i wonder if we could bring brad in i mean brad i'm sitting there wondering okay is this incremental to the market is it disruptive and replaceable what are your thoughts on this space it's already disrupted the market i mean like carl said go to any bank and ask them are you using graph databases to do to get fraud detection under control and they'll say absolutely that's the only way to solve this problem and it is frankly um and it's the only way to solve a lot of the problems that carl mentioned and that is i think it's it's achilles heel in some ways because you know it's like finding the best way to cross the seven bridges of konigsberg you know it's always going to kind of be tied to those use cases because it's really special and it's really unique and because it's special and it's unique uh it it still unfortunately kind of stands apart from the rest of the community that's building let's say ai outcomes as the great great example here the graph databases and ai as carl mentioned are like chocolate and peanut butter but technologically they don't know how to talk to one another they're completely different um and you know it's you can't just stand up sql and query them you've got to to learn um yeah what is that carlos specter or uh special uh uh yeah thank you uh to actually get to the data in there and if you're gonna scale that data that graph database especially a property graph if you're gonna do something really complex like try to understand uh you know all of the metadata in your organization you might just end up with you know a graph database winter like we had the ai winter simply because you run out of performance to make the thing happen so i i think it's already disrupted but we we need to like treat it like a first-class citizen in in the data analytics and ai community we need to bring it into the fold we need to equip it with the tools it needs to do that the magic it does and to do it not just for specialized use cases but for everything because i i'm with carl i i think it's absolutely revolutionary so i had also identified the principal achilles heel of the technology which is scaling now when these when these things get large and complex enough that they spill over what a single server can handle you start to have difficulties because the relationships span things that have to be resolved over a network and then you get network latency and that slows the system down so that's still a problem to be solved sanjeev any quick thoughts on this i mean i think metadata on the on the on the word cloud is going to be the the largest font uh but what are your thoughts here i want to like step away so people don't you know associate me with only meta data so i want to talk about something a little bit slightly different uh dbengines.com has done an amazing job i think almost everyone knows that they chronicle all the major databases that are in use today in january of 2022 there are 381 databases on its list of ranked list of databases the largest category is rdbms the second largest category is actually divided into two property graphs and rdf graphs these two together make up the second largest number of data databases so talking about accolades here this is a problem the problem is that there's so many graph databases to choose from they come in different shapes and forms uh to bright's point there's so many query languages in rdbms is sql end of the story here we've got sci-fi we've got gremlin we've got gql and then your proprietary languages so i think there's a lot of disparity in this space but excellent all excellent points sanji i must say and that is a problem the languages need to be sorted and standardized and it needs people need to have a road map as to what they can do with it because as you say you can do so many things and so many of those things are unrelated that you sort of say well what do we use this for i'm reminded of the saying i learned a bunch of years ago when somebody said that the digital computer is the only tool man has ever devised that has no particular purpose all right guys we gotta we gotta move on to dave uh meninger uh we've heard about streaming uh your prediction is in that realm so please take it away sure so i like to say that historical databases are to become a thing of the past but i don't mean that they're going to go away that's not my point i mean we need historical databases but streaming data is going to become the default way in which we operate with data so in the next say three to five years i would expect the data platforms and and we're using the term data platforms to represent the evolution of databases and data lakes that the data platforms will incorporate these streaming capabilities we're going to process data as it streams into an organization and then it's going to roll off into historical databases so historical databases don't go away but they become a thing of the past they store the data that occurred previously and as data is occurring we're going to be processing it we're going to be analyzing we're going to be acting on it i mean we we only ever ended up with historical databases because we were limited by the technology that was available to us data doesn't occur in batches but we processed it in batches because that was the best we could do and it wasn't bad and we've continued to improve and we've improved and we've improved but streaming data today is still the exception it's not the rule right there's there are projects within organizations that deal with streaming data but it's not the default way in which we deal with data yet and so that that's my prediction is that this is going to change we're going to have um streaming data be the default way in which we deal with data and and how you label it what you call it you know maybe these databases and data platforms just evolve to be able to handle it but we're going to deal with data in a different way and our research shows that already about half of the participants in our analytics and data benchmark research are using streaming data you know another third are planning to use streaming technologies so that gets us to about eight out of ten organizations need to use this technology that doesn't mean they have to use it throughout the whole organization but but it's pretty widespread in its use today and has continued to grow if you think about the consumerization of i.t we've all been conditioned to expect immediate access to information immediate responsiveness you know we want to know if an uh item is on the shelf at our local retail store and we can go in and pick it up right now you know that's the world we live in and that's spilling over into the enterprise i.t world where we have to provide those same types of capabilities um so that's my prediction historical database has become a thing of the past streaming data becomes the default way in which we we operate with data all right thank you david well so what what say you uh carl a guy who's followed historical databases for a long time well one thing actually every database is historical because as soon as you put data in it it's now history it's no longer it no longer reflects the present state of things but even if that history is only a millisecond old it's still history but um i would say i mean i know you're trying to be a little bit provocative in saying this dave because you know as well as i do that people still need to do their taxes they still need to do accounting they still need to run general ledger programs and things like that that all involves historical data that's not going to go away unless you want to go to jail so you're going to have to deal with that but as far as the leading edge functionality i'm totally with you on that and i'm just you know i'm just kind of wondering um if this chain if this requires a change in the way that we perceive applications in order to truly be manifested and rethinking the way m applications work um saying that uh an application should respond instantly as soon as the state of things changes what do you say about that i i think that's true i think we do have to think about things differently that's you know it's not the way we design systems in the past uh we're seeing more and more systems designed that way but again it's not the default and and agree 100 with you that we do need historical databases you know that that's clear and even some of those historical databases will be used in conjunction with the streaming data right so absolutely i mean you know let's take the data warehouse example where you're using the data warehouse as context and the streaming data as the present you're saying here's a sequence of things that's happening right now have we seen that sequence before and where what what does that pattern look like in past situations and can we learn from that so tony bear i wonder if you could comment i mean if you when you think about you know real-time inferencing at the edge for instance which is something that a lot of people talk about um a lot of what we're discussing here in this segment looks like it's got great potential what are your thoughts yeah well i mean i think you nailed it right you know you hit it right on the head there which is that i think a key what i'm seeing is that essentially and basically i'm going to split this one down the middle is i don't see that basically streaming is the default what i see is streaming and basically and transaction databases um and analytics data you know data warehouses data lakes whatever are converging and what allows us technically to converge is cloud native architecture where you can basically distribute things so you could have you can have a note here that's doing the real-time processing that's also doing it and this is what your leads in we're maybe doing some of that real-time predictive analytics to take a look at well look we're looking at this customer journey what's happening with you know you know with with what the customer is doing right now and this is correlated with what other customers are doing so what i so the thing is that in the cloud you can basically partition this and because of basically you know the speed of the infrastructure um that you can basically bring these together and or and so and kind of orchestrate them sort of loosely coupled manner the other part is that the use cases are demanding and this is part that goes back to what dave is saying is that you know when you look at customer 360 when you look at let's say smart you know smart utility grids when you look at any type of operational problem it has a real-time component and it has a historical component and having predictives and so like you know you know my sense here is that there that technically we can bring this together through the cloud and i think the use case is that is that we we can apply some some real-time sort of you know predictive analytics on these streams and feed this into the transactions so that when we make a decision in terms of what to do as a result of a transaction we have this real time you know input sanjeev did you have a comment yeah i was just going to say that to this point you know we have to think of streaming very different because in the historical databases we used to bring the data and store the data and then we used to run rules on top uh aggregations and all but in case of streaming the mindset changes because the rules normally the inference all of that is fixed but the data is constantly changing so it's a completely reverse way of thinking of uh and building applications on top of that so dave menninger there seemed to be some disagreement about the default or now what kind of time frame are you are you thinking about is this end of decade it becomes the default what would you pin i i think around you know between between five to ten years i think this becomes the reality um i think you know it'll be more and more common between now and then but it becomes the default and i also want sanjeev at some point maybe in one of our subsequent conversations we need to talk about governing streaming data because that's a whole other set of challenges we've also talked about it rather in a two dimensions historical and streaming and there's lots of low latency micro batch sub second that's not quite streaming but in many cases it's fast enough and we're seeing a lot of adoption of near real time not quite real time as uh good enough for most for many applications because nobody's really taking the hardware dimension of this information like how do we that'll just happen carl so near real time maybe before you lose the customer however you define that right okay um let's move on to brad brad you want to talk about automation ai uh the the the pipeline people feel like hey we can just automate everything what's your prediction yeah uh i'm i'm an ai fiction auto so apologies in advance for that but uh you know um i i think that um we've been seeing automation at play within ai for some time now and it's helped us do do a lot of things for especially for practitioners that are building ai outcomes in the enterprise uh it's it's helped them to fill skills gaps it's helped them to speed development and it's helped them to to actually make ai better uh because it you know in some ways provides some swim lanes and and for example with technologies like ottawa milk and can auto document and create that sort of transparency that that we talked about a little bit earlier um but i i think it's there's an interesting kind of conversion happening with this idea of automation um and and that is that uh we've had the automation that started happening for practitioners it's it's trying to move outside of the traditional bounds of things like i'm just trying to get my features i'm just trying to pick the right algorithm i'm just trying to build the right model uh and it's expanding across that full life cycle of building an ai outcome to start at the very beginning of data and to then continue on to the end which is this continuous delivery and continuous uh automation of of that outcome to make sure it's right and it hasn't drifted and stuff like that and because of that because it's become kind of powerful we're starting to to actually see this weird thing happen where the practitioners are starting to converge with the users and that is to say that okay if i'm in tableau right now i can stand up salesforce einstein discovery and it will automatically create a nice predictive algorithm for me um given the data that i that i pull in um but what's starting to happen and we're seeing this from the the the companies that create business software so salesforce oracle sap and others is that they're starting to actually use these same ideals and a lot of deep learning to to basically stand up these out of the box flip a switch and you've got an ai outcome at the ready for business users and um i i'm very much you know i think that that's that's the way that it's going to go and what it means is that ai is is slowly disappearing uh and i don't think that's a bad thing i think if anything what we're going to see in 2022 and maybe into 2023 is this sort of rush to to put this idea of disappearing ai into practice and have as many of these solutions in the enterprise as possible you can see like for example sap is going to roll out this quarter this thing called adaptive recommendation services which which basically is a cold start ai outcome that can work across a whole bunch of different vertical markets and use cases it's just a recommendation engine for whatever you need it to do in the line of business so basically you're you're an sap user you look up to turn on your software one day and you're a sales professional let's say and suddenly you have a recommendation for customer churn it's going that's great well i i don't know i i think that's terrifying in some ways i think it is the future that ai is going to disappear like that but i am absolutely terrified of it because um i i think that what it what it really does is it calls attention to a lot of the issues that we already see around ai um specific to this idea of what what we like to call it omdia responsible ai which is you know how do you build an ai outcome that is free of bias that is inclusive that is fair that is safe that is secure that it's audible etc etc etc etc that takes some a lot of work to do and so if you imagine a customer that that's just a sales force customer let's say and they're turning on einstein discovery within their sales software you need some guidance to make sure that when you flip that switch that the outcome you're going to get is correct and that's that's going to take some work and so i think we're going to see this let's roll this out and suddenly there's going to be a lot of a lot of problems a lot of pushback uh that we're going to see and some of that's going to come from gdpr and others that sam jeeve was mentioning earlier a lot of it's going to come from internal csr requirements within companies that are saying hey hey whoa hold up we can't do this all at once let's take the slow route let's make ai automated in a smart way and that's going to take time yeah so a couple predictions there that i heard i mean ai essentially you disappear it becomes invisible maybe if i can restate that and then if if i understand it correctly brad you're saying there's a backlash in the near term people can say oh slow down let's automate what we can those attributes that you talked about are non trivial to achieve is that why you're a bit of a skeptic yeah i think that we don't have any sort of standards that companies can look to and understand and we certainly within these companies especially those that haven't already stood up in internal data science team they don't have the knowledge to understand what that when they flip that switch for an automated ai outcome that it's it's gonna do what they think it's gonna do and so we need some sort of standard standard methodology and practice best practices that every company that's going to consume this invisible ai can make use of and one of the things that you know is sort of started that google kicked off a few years back that's picking up some momentum and the companies i just mentioned are starting to use it is this idea of model cards where at least you have some transparency about what these things are doing you know so like for the sap example we know for example that it's convolutional neural network with a long short-term memory model that it's using we know that it only works on roman english uh and therefore me as a consumer can say oh well i know that i need to do this internationally so i should not just turn this on today great thank you carl can you add anything any context here yeah we've talked about some of the things brad mentioned here at idc in the our future of intelligence group regarding in particular the moral and legal implications of having a fully automated you know ai uh driven system uh because we already know and we've seen that ai systems are biased by the data that they get right so if if they get data that pushes them in a certain direction i think there was a story last week about an hr system that was uh that was recommending promotions for white people over black people because in the past um you know white people were promoted and and more productive than black people but not it had no context as to why which is you know because they were being historically discriminated black people being historically discriminated against but the system doesn't know that so you know you have to be aware of that and i think that at the very least there should be controls when a decision has either a moral or a legal implication when when you want when you really need a human judgment it could lay out the options for you but a person actually needs to authorize that that action and i also think that we always will have to be vigilant regarding the kind of data we use to train our systems to make sure that it doesn't introduce unintended biases and to some extent they always will so we'll always be chasing after them that's that's absolutely carl yeah i think that what you have to bear in mind as a as a consumer of ai is that it is a reflection of us and we are a very flawed species uh and so if you look at all the really fantastic magical looking supermodels we see like gpt three and four that's coming out z they're xenophobic and hateful uh because the people the data that's built upon them and the algorithms and the people that build them are us so ai is a reflection of us we need to keep that in mind yeah we're the ai's by us because humans are biased all right great okay let's move on doug henson you know a lot of people that said that data lake that term's not not going to not going to live on but it appears to be have some legs here uh you want to talk about lake house bring it on yes i do my prediction is that lake house and this idea of a combined data warehouse and data lake platform is going to emerge as the dominant data management offering i say offering that doesn't mean it's going to be the dominant thing that organizations have out there but it's going to be the predominant vendor offering in 2022. now heading into 2021 we already had cloudera data bricks microsoft snowflake as proponents in 2021 sap oracle and several of these fabric virtualization mesh vendors join the bandwagon the promise is that you have one platform that manages your structured unstructured and semi-structured information and it addresses both the beyond analytics needs and the data science needs the real promise there is simplicity and lower cost but i think end users have to answer a few questions the first is does your organization really have a center of data gravity or is it is the data highly distributed multiple data warehouses multiple data lakes on-premises cloud if it if it's very distributed and you you know you have difficulty consolidating and that's not really a goal for you then maybe that single platform is unrealistic and not likely to add value to you um you know also the fabric and virtualization vendors the the mesh idea that's where if you have this highly distributed situation that might be a better path forward the second question if you are looking at one of these lake house offerings you are looking at consolidating simplifying bringing together to a single platform you have to make sure that it meets both the warehouse need and the data lake need so you have vendors like data bricks microsoft with azure synapse new really to the data warehouse space and they're having to prove that these data warehouse capabilities on their platforms can meet the scaling requirements can meet the user and query concurrency requirements meet those tight slas and then on the other hand you have the or the oracle sap snowflake the data warehouse uh folks coming into the data science world and they have to prove that they can manage the unstructured information and meet the needs of the data scientists i'm seeing a lot of the lake house offerings from the warehouse crowd managing that unstructured information in columns and rows and some of these vendors snowflake in particular is really relying on partners for the data science needs so you really got to look at a lake house offering and make sure that it meets both the warehouse and the data lake requirement well thank you doug well tony if those two worlds are going to come together as doug was saying the analytics and the data science world does it need to be some kind of semantic layer in between i don't know weigh in on this topic if you would oh didn't we talk about data fabrics before common metadata layer um actually i'm almost tempted to say let's declare victory and go home in that this is actually been going on for a while i actually agree with uh you know much what doug is saying there which is that i mean we i remembered as far back as i think it was like 2014 i was doing a a study you know it was still at ovum predecessor omnia um looking at all these specialized databases that were coming up and seeing that you know there's overlap with the edges but yet there was still going to be a reason at the time that you would have let's say a document database for json you'd have a relational database for tran you know for transactions and for data warehouse and you had you know and you had basically something at that time that that resembles to do for what we're considering a day of life fast fo and the thing is what i was saying at the time is that you're seeing basically blur you know sort of blending at the edges that i was saying like about five or six years ago um that's all and the the lake house is essentially you know the amount of the the current manifestation of that idea there is a dichotomy in terms of you know it's the old argument do we centralize this all you know you know in in in in in a single place or do we or do we virtualize and i think it's always going to be a yin and yang there's never going to be a single single silver silver bullet i do see um that they're also going to be questions and these are things that points that doug raised they're you know what your what do you need of of of your of you know for your performance there or for your you know pre-performance characteristics do you need for instance hiking currency you need the ability to do some very sophisticated joins or is your requirement more to be able to distribute and you know distribute our processing is you know as far as possible to get you know to essentially do a kind of brute force approach all these approaches are valid based on you know based on the used case um i just see that essentially that the lake house is the culmination of it's nothing it's just it's a relatively new term introduced by databricks a couple years ago this is the culmination of basically what's been a long time trend and what we see in the cloud is that as we start seeing data warehouses as a checkbox item say hey we can basically source data in cloud and cloud storage and s3 azure blob store you know whatever um as long as it's in certain formats like you know like you know parquet or csv or something like that you know i see that as becoming kind of you know a check box item so to that extent i think that the lake house depending on how you define it is already reality um and in some in some cases maybe new terminology but not a whole heck of a lot new under the sun yeah and dave menger i mean a lot of this thank you tony but a lot of this is going to come down to you know vendor marketing right some people try to co-opt the term we talked about data mesh washing what are your thoughts on this yeah so um i used the term data platform earlier and and part of the reason i use that term is that it's more vendor neutral uh we've we've tried to uh sort of stay out of the the vendor uh terminology patenting world right whether whether the term lake house is what sticks or not the concept is certainly going to stick and we have some data to back it up about a quarter of organizations that are using data lakes today already incorporate data warehouse functionality into it so they consider their data lake house and data warehouse one in the same about a quarter of organizations a little less but about a quarter of organizations feed the data lake from the data warehouse and about a quarter of organizations feed the data warehouse from the data lake so it's pretty obvious that three quarters of organizations need to bring this stuff together right the need is there the need is apparent the technology is going to continue to verge converge i i like to talk about you know you've got data lakes over here at one end and i'm not going to talk about why people thought data lakes were a bad idea because they thought you just throw stuff in a in a server and you ignore it right that's not what a data lake is so you've got data lake people over here and you've got database people over here data warehouse people over here database vendors are adding data lake capabilities and data lake vendors are adding data warehouse capabilities so it's obvious that they're going to meet in the middle i mean i think it's like tony says i think we should there declare victory and go home and so so i it's just a follow-up on that so are you saying these the specialized lake and the specialized warehouse do they go away i mean johnny tony data mesh practitioners would say or or advocates would say well they could all live as just a node on the on the mesh but based on what dave just said are we going to see those all morph together well number one as i was saying before there's always going to be this sort of you know kind of you know centrifugal force or this tug of war between do we centralize the data do we do it virtualize and the fact is i don't think that work there's ever going to be any single answer i think in terms of data mesh data mesh has nothing to do with how you physically implement the data you could have a data mesh on a basically uh on a data warehouse it's just that you know the difference being is that if we use the same you know physical data store but everybody's logically manual basically governing it differently you know um a data mission is basically it's not a technology it's a process it's a governance process um so essentially um you know you know i basically see that you know as as i was saying before that this is basically the culmination of a long time trend we're essentially seeing a lot of blurring but there are going to be cases where for instance if i need let's say like observe i need like high concurrency or something like that there are certain things that i'm not going to be able to get efficiently get out of a data lake um and you know we're basically i'm doing a system where i'm just doing really brute forcing very fast file scanning and that type of thing so i think there always will be some delineations but i would agree with dave and with doug that we are seeing basically a a confluence of requirements that we need to essentially have basically the element you know the ability of a data lake and a data laid out their warehouse we these need to come together so i think what we're likely to see is organizations look for a converged platform that can handle both sides for their center of data gravity the mesh and the fabric vendors the the fabric virtualization vendors they're all on board with the idea of this converged platform and they're saying hey we'll handle all the edge cases of the stuff that isn't in that center of data gradient that is off distributed in a cloud or at a remote location so you can have that single platform for the center of of your your data and then bring in virtualization mesh what have you for reaching out to the distributed data bingo as they basically said people are happy when they virtualize data i i think yes at this point but to this uh dave meningas point you know they have convert they are converging snowflake has introduced support for unstructured data so now we are literally splitting here now what uh databricks is saying is that aha but it's easy to go from data lake to data warehouse than it is from data warehouse to data lake so i think we're getting into semantics but we've already seen these two converge so is that so it takes something like aws who's got what 15 data stores are they're going to have 15 converged data stores that's going to be interesting to watch all right guys i'm going to go down the list and do like a one i'm going to one word each and you guys each of the analysts if you wouldn't just add a very brief sort of course correction for me so sanjeev i mean governance is going to be the maybe it's the dog that wags the tail now i mean it's coming to the fore all this ransomware stuff which really didn't talk much about security but but but what's the one word in your prediction that you would leave us with on governance it's uh it's going to be mainstream mainstream okay tony bear mesh washing is what i wrote down that's that's what we're going to see in uh in in 2022 a little reality check you you want to add to that reality check is i hope that no vendor you know jumps the shark and calls their offering a data mesh project yeah yeah let's hope that doesn't happen if they do we're going to call them out uh carl i mean graph databases thank you for sharing some some you know high growth metrics i know it's early days but magic is what i took away from that it's the magic database yeah i would actually i've said this to people too i i kind of look at it as a swiss army knife of data because you can pretty much do anything you want with it it doesn't mean you should i mean that's definitely the case that if you're you know managing things that are in a fixed schematic relationship probably a relational database is a better choice there are you know times when the document database is a better choice it can handle those things but maybe not it may not be the best choice for that use case but for a great many especially the new emerging use cases i listed it's the best choice thank you and dave meninger thank you by the way for bringing the data in i like how you supported all your comments with with some some data points but streaming data becomes the sort of default uh paradigm if you will what would you add yeah um i would say think fast right that's the world we live in you got to think fast fast love it uh and brad shimon uh i love it i mean on the one hand i was saying okay great i'm afraid i might get disrupted by one of these internet giants who are ai experts so i'm gonna be able to buy instead of build ai but then again you know i've got some real issues there's a potential backlash there so give us the there's your bumper sticker yeah i i would say um going with dave think fast and also think slow uh to to talk about the book that everyone talks about i would say really that this is all about trust trust in the idea of automation and of a transparent invisible ai across the enterprise but verify verify before you do anything and then doug henson i mean i i look i think the the trend is your friend here on this prediction with lake house is uh really becoming dominant i liked the way you set up that notion of you know the the the data warehouse folks coming at it from the analytics perspective but then you got the data science worlds coming together i still feel as though there's this piece in the middle that we're missing but your your final thoughts we'll give you the last well i think the idea of consolidation and simplification uh always prevails that's why the appeal of a single platform is going to be there um we've already seen that with uh you know hadoop platforms moving toward cloud moving toward object storage and object storage becoming really the common storage point for whether it's a lake or a warehouse uh and that second point uh i think esg mandates are uh are gonna come in alongside uh gdpr and things like that to uh up the ante for uh good governance yeah thank you for calling that out okay folks hey that's all the time that that we have here your your experience and depth of understanding on these key issues and in data and data management really on point and they were on display today i want to thank you for your your contributions really appreciate your time enjoyed it thank you now in addition to this video we're going to be making available transcripts of the discussion we're going to do clips of this as well we're going to put them out on social media i'll write this up and publish the discussion on wikibon.com and siliconangle.com no doubt several of the analysts on the panel will take the opportunity to publish written content social commentary or both i want to thank the power panelist and thanks for watching this special cube presentation this is dave vellante be well and we'll see you next time [Music] you

>>mm We're entering the fourth grade era of VM ware Executive management From its beginnings in the late 90s is a Silicon Valley startup. It's five founders quickly built the company and it ended up as one of the greatest acquisitions in the history of enterprise tech when EMC bought VM ware for $625 million as a public company. But still under EMC's governance, paul Maritz was appointed Ceo in 2000 and eight and set the company on a journey to build what he called at the time. The software mainframe meaning the company's platform would run any application at high performance with low overhead and world class recovery. Pat Gelsinger took over the Ceo reins in 2012 and through organic investments and clever m and a set of course for the software defined data center and after some early miscalculations in cloud, realigned the company strategy to successfully partner with hyper scale hours and position the company for the multi cloud future. The hallmarks of VM where over the course of its history have been great engineering that led to great products, loyal customers and a powerful ecosystem. The other telling attribute of VM where is it? CEOs have always had a deep understanding of technology and its latest Ceo is no different. It's our pleasure to welcome raghu Raghuram back to the cube the fourth Ceo of VM ware and yet another Silicon Valley Ceo graduated from the IIttie rgu, great to see you again and congratulations on your new role. >>Thanks. It's great to be here. >>Okay, five months in 1st 100 days what we have focused on that journey to become the Switzerland of multi cloud, tell us about your early experience as ceo >>it's been fantastic. Uh our customers, all our employees, all our partners have been very welcoming and of course I've given me great input. What we've been able to do in the last 100 days is to really crystallize the strategy and focus it around what I'm sure we're gonna be spending a lot of time talking about. And that's about the multi cloud era of computing that most enterprises are going to go through over the next decade. And so that's really what I've been up to and you'll see the results of that in next week's uh we involved and uh where we would be talking about the strategy and some product announcements that go along with the strategy and so it's a very exciting time to be at Vandenberg. >>Yeah, I mean, I referenced it in my intro, it's almost like the light bulb went off when VM ware realized, wow, this cloud build out is just an opportunity for us and that's really what you're doing with the multi cloud as you're building on top of all the infrastructure that the hyper cloud vendors are putting out there. Maybe you can talk about that, that opportunity and what customers are telling you. >>Yeah, it's uh here is how I describe what has happened in the industry. Right, and what will happen in the industry. So, if you look at the the past decade, as cloud became a mainstream thing, most customers pick the cloud, they built their first digital applications into it, the ones that serve their mobile users or end users with digital products and that worked great for them. Then they step back and say, okay, how many modernize everything that we're doing has become a digital company. And when you go from 10, of your portfolio, 100% of your application portfolio being modernized. What has to happen is you got to go from figuring out, okay, how am I gonna put everything in one cloud to what does the application need and how do I put it on the right place? I look at the same time, the industry has also evolved from being uh predominantly supplied by one cloud provider to multiple cloud providers. At the same time, the thanks to companies like IBM where the data center has been transformed into a private cloud. The edges growing up to be its own location for a cloud sovereign clouds are going. So truly what has happened is it's become a multi cloud world. And customers are saying in addition to just being cloud first, I want to be cloud smart. And so this distributed era of computing that we are entering is what we are seeing in the industry. And what the empire is trying to do is to say, look, let's provide customers with the fastest way of getting to this multi cloud era of computing so that they can go fast, they can spend less and most importantly, they can be free, in other words, choose the right application, right cloud for right applications and have control over how they deploy and use their applications and data. That really is a strategy that we are putting in place. This is something that we've been working towards in the last couple of years now. I'm accelerating that and making that the main piece of what we end, where is doing in order to do that, we have a great opportunity to take partner even better with all of our cloud provider partners and that's where the Switzerland of the industry comes in without impending spin, especially, we have great partnership with the cloud players, great partnerships with infrastructure players. We truly can be a neutral partner to the customers as they look at all these choices and make the right choices for their applications. >>So, I want to ask you about this multi cloud when when the early multi cloud narrative came out where I go, I was saying, look, multi cloud is really multi vendor, you you've got workloads and apps running on different, different clouds. And then increasingly, the promise and your promises, we're going to abstract the underlying complexity of those clouds and we're going to give you an experience whether it's on premise, hybrid into a cloud. Across clouds. Eventually out to the edge, it's gonna be a singular, substantially identical, if not identical experience and we're going to manage the whole kit and caboodle. And how where are we in that first of all? Is that the right way to think about it? Where are we in that sort of transition from plugging into any, you know, a cloud? I'm compatible with the cloud to it's a singular sort of VM ware cloud if you will. >>Yeah. So, um, so I wanna clarify something that he said because this tends to be very commonly confused by customers use the word abstraction. And usually when people think of abstraction, they think it hides capabilities of the uh, cloud providers. That's not what we are trying to. In fact, that's the last thing we're trying to do. What we're trying to do is to provide a consistent developer experience regardless of where you want to build your application so that you can use the cloud provider services if that's what you want to use. But the deficit cops toolchain, the runtime environment, which turns out to be Cuban aires and how you control the kubernetes environment. How do you manage and secure and connect all of these things. Those are the places where we are adding the value. Right. And so really the VM ware value proposition is you can build on the cloud of your choice but providing these consistent elements. Number one, you can make better use of us, your scarce developer or operator resources and expertise. Right. And number two, you can move faster and number three can spend less as a result of this. So that's really what we're trying to do, but not. So I just wanted to clarify the word abstraction in terms of their way, we're still, I would say in the early stages, so if you look at what customers are trying to do, they're trying to build these Greenfield applications and there is an entire ecosystem emerging around Campaneris. There is still kubernetes is not a developer platform. The developer experience on top of kubernetes is highly inconsistent. And so those are some of the areas where we are introducing new innovations with our towns, our application platform. And then if you take enterprise applications, what does it take to have enterprise applications running all the time, be entirely secure, etcetera, etcetera. That's where the we ever cloud assets that are traditionally this fear based come into play and we've got this now in all of the clouds but it's still in the early days from uh on Azure and google et cetera. How do you manage and secure those things again? We're in the early days. So that's where we are. I would say, >>yeah, thank you for that clarification, I want to sort of come back to that and just make sure we understand it. So for example, if I'm a developer and I want to take advantage of, let's say graviton uh and build an app on that, that so maybe it's some kind of data intensive app or whatever it is. I can do that. You won't restrict me from doing that at the same time. If I want to use the VM where management experience across all my clouds, I can do that as well. Is that the right way to think about it? >>Yeah, exactly. So the management experience by the way, and this is the other thing that gets missed in the remember dialogue because we've been so phenomenally successful with this fear. There's a misperception that everything we are doing atmosphere today works only on top. So everything we're doing at BM wear works only on top of the sphere. That's not the case. Take management, for example, our management portfolio is modular and independent of these, which means it can manage the Graviton application that you're building, right. It can manage a traditional, these fear based application, it can manage rage application, it can manage VM based applications, can manage computer based applications. Uh so it's truly uh, overall management layer. So that is really what we're trying to do. Same thing with our kubernetes example. Right, So our communities control plane allows you to control these kubernetes clusters. Whether the clusters are utilizing gravity and whether clusters are utilizing these fear based crew binaries environments. >>Okay, that's great. So it's kind of a set up question because my next question relates to project Monterey, Because, you know, I've always said when I write about about these things, when I saw Nitro, I saw Graviton, I saw project monitor, I said uh everybody needs a Nitro Nitro or a graviton because new workloads are coming. It's not just the X 86 can handle everything anymore asap whether it's sequel server, whatever we've got new workloads that are coming ai ml data intensive edge workloads, et cetera. Is that how we should think of? Project Monterrey. Where are you in Project Monterey? Why is it so important? Help people understand that? >>Yeah. Project mantra is super exciting for a couple of different reasons. One is uh in its first iteration and uh we announced project monitoring and last being well, we continue to build and we're making great progress along with the hardware partners that we are working with um in its first hydration it allows um um some of the functions that you would expect in the software defined data center to be offloaded into these montri processors. The smart nick processes. Right. So what that does, is it clears up the core CPU for other application functions. Right, so you get better scalability, more resource utilization, etcetera, etcetera. The second thing it does is because some of the software defined data center functions are done in the smart make um it gets accelerated as well. Because it takes advantage of the special accelerators that are there security functions, manageability functions, networking functions etcetera, etcetera. So that's that what you're alluding to is overall it's the v sphere, the sX Hyper Visor complimentary itself. That's moving into the specialized processors which allows the hyper Visor will be built into these smart mix, which means the main CPU can be an intel. CPU can be an M D C P. You can be an arm. CPU can be whatever it is you want in the future. So truly enables Monte CPU heterogeneous computing. So that's that's why this is exciting. And of course because it is the sphere, it can happen in the data centers, it can happen in Carlos. It can happen in Sovereign clouds. It can happen in the public clouds all over a period of time. And >>and potentially the Edge I would presume in the future. >>Sorry. Yeah, that's a great point. Thanks for pointing that out. In fact, the Edge is one of the most important places that will happen because we need these low latency applications such as in the telco case for example, right. Or we need these applications that have specialized processing the required. If you're setting up a cashier less store and you need to process and you need a lot of influence engines. So, Monterey helps with all of those things. >>I want to make sure our audience understands. It's because the software defined data center was awesome but but it also created waste in the sense that you have all these offload functions in storage and networking and security running on on x 86 processors which may not be the most efficient way. So emerging architectures around arm might be less expensive, maybe more cost effective, lower power. Uh maybe they do memory management differently. So there are these offload use cases. But as well you we talked about the edge there could be a lot of edge use cases that or whatever whether it's arm or in video etcetera. So now you're driving that optionality for customers so you can support more workloads of the future. >>Yeah, so this is exactly if you think about in europe when you talked about the embers evolution, the inverse core DNA has always been to master hydrogen. Itty right. And what we're seeing is this world of heterogeneous hardware coming alive. Right. You talked about Professor hydrogen Itty including GPU chips and so on. There is a memory architecture heterogeneous, their storage architecture heterogeneous. And so the idea is that regardless of what you use, how do you provide the best workload platform and a consistent way of managing all of these things and reducing the complexity while gaining the efficiency benefits and the other benefits that you talked about. >>So speaking of geniality that brings me to Tansu, you know early on people thought, oh wow containers, that's gonna kill VM where this is the opposite happened. You guys leaned in as as you have as a sign of great leadership these days. You don't get defensive, you just, you know, get the trend is your friend, as they say, give us the update on on Tan xue. Why is that so important to the future? >>Yeah. So if you look at any enterprises portfolio right, they are looking at it and saying look, there's a whole set of applications that I need to modernize. Now. The question becomes how do you modernize these applications in a way that it is essentially done with these microservices architectures and so on and so forth. In that context, how do I maximize the developer productivity and provide a great developer experience because there is not enough developers in the world to modernize every application that that's in every enterprise. Right. So, Tan xue is our answer to help enterprises modernize their applications and deliver in a way that the developed makes the developers very productive on the cloud of their choice. So that is really the strategic intent of Tancill and the core building block for Tan xue is of course kubernetes as you well know, Kubernetes has become the common infrastructure abstraction across clouds. So if you want portability for traditional VM based applications, he used this fear, if you want portability for traditional for containerized microservices applications, you assume kubernetes, that's how companies companies are thinking about it. And so that's the first thing that we did now. The second is you've got developers building applications all over the place. So now, just like you used to have physical server sprawl and now and then VM sprawl these days you have cluster sprawl, kubernetes, cluster sprawl and tons of mission control affects as a multi cloud, multi cluster kubernetes control plan works on the chaos and everything else that some of the Sun. The third point of Tanzania is the developer experience and we have introduced Andrew application platform, which is really focused on delivering a great developer experience on top of any Cuban Aires. So that's really how we're building out the towns of portfolio. And then of course we got Spring and uh as you well know a majority of enterprise applications today are java and if you want to modernize java, you use spring boot and so we had tremendous success with our uh spring boot technology and our startup, Springdale Ohio capabilities and so on and so forth. So that's the entirety of the towns of portfolio. It's multi cloud, it's kubernetes agnostic. Of course it runs great on this fear but it's really the approach making developers productive in the enterprise >>awesome. Thank you for that. I know we're tight on time but it's like speed dating with you raghu. So I'm gonna go on to another topic. Really important topic of security, you've made obviously some big acquisitions, there are things like carbon black, you've got a lot of stuff going on with, with, with endpoint, with end user computing, I'm first interested in sort of how you organize it looks like you're putting security and the networking piece together and then what's your swim lane? It seems like you're, you're focused obviously on your infrastructure. You're not trying to be all things to all people. Help us understand your strategy in that regard. >>Yeah, I mean security is a massive space, Right? And you covered very well. Hundreds and thousands of security problems that customers want to be solving. What we are focused on is how do we simplify the security problem for the customers? And we're doing it through three wells. The first one is we are baking security into the platforms that customers used ones. Right. But there are more obvious fear our workspace one, our container platform etcetera, etcetera. Right? Cloud platforms. So that's the first thing that we're doing. The second is we are putting um, bringing together, we're taking an end to end view of security, which is everything from an end user connecting from home to the corporate network or the sassy, sassy applications to the Windows devices they are using to the data center applications they're using to the club. Right? So we're taking a holistic view of security. So which means we want to combine our network security assets with our endpoint security assets with our workload security assets. That is why we bought all of those things together under one roof. And the third is we are instrumental in all of these and collecting signals from all of these and pulling it into the cloud and turning security into a machine learning and the data problem, right? And that is where the problem. Black cloud comes in and by doing that, we are able to provide a holistic view of where uh customer security posture, right? And these sensors can be on BMR platforms, on non BMR platforms etcetera. And so so that's really how we are approaching it. I mean there's the emerging industry term for a policy XDR. You might follow that. So that's really what we're trying to do. >>Outstanding. Last question and I know, I know we got to go. You mentioned the spin that's happening in november. That's an exciting time for a lot of reasons. I think the ecosystem, you know, emphasizing your independence but also gives you control of your balance sheet, regaining control of your balance sheet, tongue in cheek there. But it's important because all this, this cloud build out this multi cloud, exposing the primitives, leveraging the primitives and the A. P. I. S. Of these clouds making them identical across all these estates. That's not trivial and you're obviously gonna need resources to do that. So maybe you can talk about that and how you see the future playing out organic inorganic, maybe a little lemon A in there. What's your approach? How are you thinking about that? >>Yeah. So we are very excited with the impending spain, which like you said is on track to happen early november. Um and if you think about the spin, there are three aspects that we are excited about. The first aspect is uh we have a great relationship with Dell Tech, the company right. What we have done is we have codified that into a framework agreement that covers the gold market and technology collaboration and we are super excited by that and that baselines against what we do today and then as incentives on both sides to continue to grow that tremendously. So we're gonna continue being, doing that and that's going to continue being a great partner at the same time. From a partnership point of view, is truly going to be a Switzerland of the industry. So previously companies that were otherwise a little bit more competitive with dull now no longer have that reservation in partnering very deeply with us. I'm totally, like you said from a capital structure point of view, it gives us the flexibility to use to do em in a should we decide to do so in the future right? And use both equity and cash for them in a so so that's the capital structure, flexibility, the Switzerland positioning and the continuing great relationship with dull Those are the benefits of the spin >>love and the partner ecosystem has always been a source of, of innovation and it's a big part of the flywheel, the power of many versus the resources of one Ragu, Thanks so much for coming back in the queue. Best of luck. We're really excited for you and for the future of VM ware. >>Thank you and thanks for all the great work that you do and look forward to continuing to read your great research, >>appreciate that. And thank you for >>watching the cubes, continuous >>Coverage of VM World 2021. Keep it right there. >>Thank you. Mhm. Yeah.

>>Hey, welcome to the live panel. My name is Brett. I am your host, and indeed we are live. In fact, if you're curious about that, if you don't believe us, um, let's just show a little bit of the browser real quick to see. Yup. There you go. We're live. So, all right. So how this is going to work is I'm going to bring in some guests and, uh, in one second, and we're going to basically take your questions on the topic designer of the day, that continuous integration testing. Uh, thank you so much to my guests welcoming into the panel. I've got Carlos, Nico and Mandy. Hello everyone. >>Hello? All right, >>Let's go. Let's go around the room and all pretend we don't know each other and that the internet didn't read below the video who we are. Uh, hi, my name is Brett. I am a Docker captain, which means I'm supposed to know something about Docker. I'm coming from Virginia Beach. I'm streaming here from Virginia Beach, Virginia, and, uh, I make videos on the internet and courses on you to me, Carlos. Hey, >>Hey, what's up? I'm Carlos Nunez. I am a solutions architect, VMware. I do solution things with computers. It's fun. I live in Dallas when I'm moving to Houston in a month, which is where I'm currently streaming. I've been all over the Northeast this whole week. So, um, it's been fun and I'm excited to meet with all of you and talk about CIA and Docker. Sure. >>Yeah. Hey everyone. Uh, Nico, Khobar here. I'm a solution engineer at HashiCorp. Uh, I am streaming to you from, uh, the beautiful Austin, Texas. Uh, ignore, ignore the golden gate bridge here. This is from my old apartment in San Francisco. Uh, just, uh, you know, keeping that, to remember all the good days, um, that that lived at. But, uh, anyway, I work at Patrick Corp and I work on all things, automation, um, and cloud and dev ops. Um, and I'm excited to be here and Mandy, >>Hi. Yeah, Mandy Hubbard. I am streaming from Austin, Texas. I am, uh, currently a DX engineer at ship engine. Um, I've worked in QA and that's kind of where I got my, uh, my Docker experience and, um, uh, moving into DX to try and help developers better understand and use our products and be an advocate for them. >>Nice. Well, thank you all for joining me. Uh, I really appreciate you taking the time out of your busy schedule to be here. And so for those of you in chat, the reason we're doing this live, because it's always harder to do things live. The reason we're here is to answer a question. So we didn't come with a bunch of slides and demos or anything like that. We're here to talk amongst ourselves about ideas and really here for you. So we've, we obviously, this is about easy CII, so we're, we're going to try to keep the conversation around testing and continuous integration and all the things that that entails with containers. But we may, we may go down rabbit holes. We may go veer off and start talking about other things, and that's totally fine if it's in the realm of dev ops and containers and developer and ops workflows, like, Hey, it's, it's kinda game. >>And, uh, these people have a wide variety of expertise. They haven't done just testing, right? We, we live in a world where you all kind of have to wear many hats. So feel free to, um, ask what you think is on the top of your mind. And we'll do our best to answer. It may, might not be the best answer or the correct answer, but we're going to do our best. Um, well, let's get it start off. Uh, let's, let's get a couple of topics to start off with. Uh, th the, the easy CGI was my, one of my three ideas. Cause he's the, one of the things that I'm most excited about is the innovation we're seeing around easier testing, faster testing, automated testing, uh, because as much as we've all been doing this stuff for, you know, 15 years, since 20 years since the sort of Jenkins early days, um, it it's, it seems like it's still really hard and it's still a lot of work. >>So, um, let's go around the room real quick, and everybody can just kind of talk for a minute about like your experience with testing and maybe some of your pain points, like what you don't like about our testing world. Um, and we can talk about some pains, cause I think that will lead us to kind of talk about what, what are the things we're seeing now that might be better, uh, ideas about how to do this. I know for me, uh, testing, obviously there's the code part, but just getting it automated, but mostly getting it in the hands of developers so that they can control their own testing. And don't have to go talk to a person to run that test again, or the mysterious Jenkins platform somewhere. I keep mentioning Jenkins cause it's, it is still the dominant player out there. Um, so for me, I'm, I'm, I, I don't like it when I'm walking into a room and there's, there's only one or two people that know how the testing works or know how to make the new tests go into the testing platform and stuff like that. So I'm always trying to free those things so that any of the developers are enabled and empowered to do that stuff. So someone else, Carlos, anybody, um, >>Oh, I have a lot of opinions on that. Having been a QA engineer for most of my career. Um, the shift that we're saying is everyone is dev ops and everyone is QA. Th the issue I see is no one asked developers if they wanted to be QA. Um, and so being the former QA on the team, when there's a problem, even though I'm a developer and we're all running QA, they always tend to come to the one of the former QA engineers. And they're not really owning that responsibility and, um, and digging in. So that's kind of what I'm saying is that we're all expected to test now. And some people, well, some people don't know how it's, uh, for me it was kind of an intuitive skill. It just kind of fit with my personality, but not knowing what to look for, not knowing what to automate, not even understanding how your API end points are used by your front end to know what to test when a change is made. It's really overwhelming for developers. And, um, we're going to need to streamline that and, and hold their hands a little bit until they get their feet wet with also being QA. >>Right. Right. So, um, uh, Carlos, >>Yeah, uh, testing is like, Tesla is one of my favorite subjects to talk about when I'm baring with developers. And a lot of it is because of what Mandy said, right? Like a lot of developers now who used to write a test and say, Hey, QA, go. Um, I wrote my unit tests. Now write the rest of the test. Essentially. Now developers are expected to be able to understand how testing, uh, testing methodologies work, um, in their local environments, right? Like they're supposed to understand how to write an integration tasks federate into and tasks, a component test. And of course, how to write unit tests that aren't just, you know, assert true is true, right? Like more comprehensive, more comprehensive, um, more high touch unit tests, which include things like mocking and stubbing and spine and all that stuff. And, you know, it's not so much getting those tests. Well, I've had a lot of challenges with developers getting those tests to run in Docker because of usually because of dependency hell, but, um, getting developers to understand how to write tests that matter and mean something. Um, it's, it's, it can be difficult, but it's also where I find a lot of the enjoyment of my work comes into play. So yeah. I mean, that's the difficulty I've seen around testing. Um, big subject though. Lots to talk about there. >>Yeah. We've got, we've already got so many questions coming in. You already got an hour's worth of stuff. So, uh, Nico 81st thoughts on that? >>Yeah, I think I definitely agree with, with other folks here on the panel, I think from a, um, the shift from a skillset perspective that's needed to adopt the new technologies, but I think from even from, uh, aside from the organizational, um, and kind of key responsibilities that, that the new developers have to kinda adapt to and, and kind of inherit now, um, there's also from a technical perspective as there's, you know, um, more developers are owning the full stack, including the infrastructure piece. So that adds a lot more to the plate in Tim's oaf, also testing that component that they were not even, uh, responsible for before. Um, and, um, also the second challenge that, you know, I'm seeing is that on, you know, the long list of added, um, uh, tooling and, you know, there's new tool every other day. Um, and, um, that kind of requires more customization to the testing, uh, that each individual team, um, any individual developer Y by extension has to learn. Uh, so the customization, uh, as well as the, kind of the scope that had, uh, you know, now in conferences, the infrastructure piece, um, uh, both of act to the, to the challenges that we're seeing right now for, um, for CGI and overall testing, um, uh, the developers are saying, uh, in, in the market today. >>Yeah. We've got a lot of questions, um, about all the, all the different parts of this. So, uh, let me just go straight to them. Cause that's why we're here is for the people, uh, a lot of people asking about your favorite tools and in one of this is one of the challenges with integration, right? Is, um, there is no, there are dominant players, but there, there is such a variety. I mean, every one of my customers seems like they're using a different workflow and a different set of tools. So, and Hey, we're all here to just talk about what we're, what we're using, uh, you know, whether your favorite tools. So like a lot of the repeated questions are, what are your favorite tools? Like if you could create it from scratch, uh, what would you use? Pierre's asking, you know, GitHub actions sounds like they're a fan of GitHub actions, uh, w you know, mentioning, pushing the ECR and Docker hub and, uh, using vs code pipeline, I guess there may be talking about Azure pipelines. Um, what, what's your preferred way? So, does anyone have any, uh, thoughts on that anyone want to throw out there? Their preferred pipeline of tooling? >>Well, I have to throw out mine. I might as Jenkins, um, like kind of a honorary cloud be at this point, having spoken a couple of times there, um, all of the plugins just make the functionality. I don't love the UI, but I love that it's been around so long. It has so much community support, and there are so many plugins so that if you want to do something, you don't have to write the code it's already been tested. Um, unfortunately I haven't been able to use Jenkins in, uh, since I joined ship engine, we, most of our, um, our, our monolithic core application is, is team city. It's a dotnet application and TeamCity plays really well with.net. Um, didn't love it, uh, Ms. Jenkins. And I'm just, we're just starting some new initiatives that are using GitHub actions, and I'm really excited to learn, to learn those. I think they have a lot of the same functionality that you're looking for, but, um, much more simplified in is right there and get hubs. So, um, the integration is a lot more seamless, but I do have to go on record that my favorite CICT tools Jenkins. >>All right. You heard it here first people. All right. Anyone else? You're muted? I'm muted. Carlin says muted. Oh, Carla says, guest has muted themselves to Carlos. You got to unmute. >>Yes. I did mute myself because I was typing a lot, trying to, you know, try to answer stuff in the chat. And there's a lot of really dark stuff in there. That's okay. Two more times today. So yeah, it's fine. Yeah, no problem. So totally. And it's the best way to start a play more. So I'm just going to go ahead and light it up. Um, for enterprise environments, I actually am a huge fan of Jenkins. Um, it's a tool that people really understand. Um, it has stood the test of time, right? I mean, people were using Hudson, but 15 years ago, maybe longer. And, you know, the way it works, hasn't really changed very much. I mean, Jenkins X is a little different, but, um, the UI and the way it works internally is pretty familiar to a lot of enterprise environments, which is great. >>And also in me, the plugin ecosystem is amazing. There's so many plugins for everything, and you can make your own if you know, Java groovy. I'm sure there's a perfect Kotlin in there, but I haven't tried myself, but it's really great. It's also really easy to write, um, CIS code, which is something I'm a big fan of. So Jenkins files have been, have worked really well for me. I, I know that I can get a little bit more complex as you start to build your own models and such, but, you know, for enterprise enterprise CIO CD, if you want, especially if you want to roll your own or own it yourself, um, Jenkins is the bellwether and for very good reason now for my personal projects. And I see a lot on the chat here, I think y'all, y'all been agreed with me get hub actions 100%, my favorite tool right now. >>Um, I love GitHub actions. It's, it's customizable, it's modular. There's a lot of plugins already. I started using getting that back maybe a week after when GA and there was no documentation or anything. And I still, it was still my favorite CIA tool even then. Um, and you know, the API is really great. There's a lot to love about GitHub actions and, um, and I, and I use it as much as I can from my personal project. So I still have a soft spot for Travis CAI. Um, you know, they got acquired and they're a little different now trying to see, I, I can't, I can't let it go. I just love it. But, um, yeah, I mean, when it comes to Seattle, those are my tools. So light me up in the comments I will respond. Yeah. >>I mean, I, I feel with you on the Travis, the, I think, cause I think that was my first time experiencing, you know, early days get hub open source and like a free CIA tool that I could describe. I think it was the ammo back then. I don't actually remember, but yeah, it was kind of an exciting time from my experience. There was like, oh, this is, this is just there as a service. And I could just use it. It doesn't, it's like get hub it's free from my open source stuff. And so it does have a soft spot in my heart too. So yeah. >>All right. We've got questions around, um, cam, so I'm going to ask some questions. We don't have to have these answers because sometimes they're going to be specific, but I want to call them out because people in chat may have missed that question. And there's probably, you know, that we have smart people in chat too. So there's probably someone that knows the answer to these things. If, if it's not us, um, they're asking about building Docker images in Kubernetes, which to me is always a sore spot because it's Kubernetes does not build images by default. It's not meant for that out of the gate. And, uh, what is the best way to do this without having to use privileged containers, which privileged containers just implying that yeah, you, you, it probably has more privileges than by default as a container in Kubernetes. And that is a hard thing because, uh, I don't, I think Docker doesn't lie to do that out of the gate. So I don't know if anyone has an immediate answer to that. That's a pretty technical one, but if you, if you know the answer to that in chat, call it out. >>Um, >>I had done this, uh, but I'm pretty sure I had to use a privileged, um, container and install the Docker Damon on the Kubernetes cluster. And I CA I can't give you a better solution. Um, I've done the same. So, >>Yeah, uh, Chavonne asks, um, back to the Jenkins thing, what's the easiest way to integrate Docker into a Jenkins CICB pipeline. And that's one of the challenges I find with Jenkins because I don't claim to be the expert on Jenkins. Is there are so many plugins because of this, of this such a huge ecosystem. Um, when you go searching for Docker, there's a lot that comes back, right. So I, I don't actually have a preferred way because every team I find uses it differently. Um, I don't know, is there a, do you know if there's a Jenkins preferred, a default plugin? I don't even know for Docker. Oh, go ahead. Yeah. Sorry for Docker. And jacon sorry, Docker plugins for Jenkins. Uh, as someone's asking like the preferred or easy way to do that. Um, and I don't, I don't know the back into Jenkins that well, so, >>Well, th the new, the new way that they're doing, uh, Docker builds with the pipeline, which is more declarative versus the groovy. It's really simple, and their documentation is really good. They, um, they make it really easy to say, run this in this image. So you can pull down, you know, public images and add your own layers. Um, so I don't know the name of that plugin, uh, but I can certainly take a minute after this session and going and get that. Um, but if you really are overwhelmed by the plugins, you can just write your, you know, your shell command in Jenkins. You could just by, you know, doing everything in bash, calling the Docker, um, Damon directly, and then getting it working just to see that end to end, and then start browsing for plugins to see if you even want to use those. >>The plugins will allow more integration from end to end. Some of the things that you input might be available later on in the process for having to manage that yourself. But, you know, you don't have to use any of the plugins. You can literally just, you know, do a block where you write your shell command and get it working, and then decide if, for plugins for you. Um, I think it's always under important to understand what is going on under the hood before you, before you adopt the magic of a plugin, because, um, once you have a problem, if you're, if it's all a lockbox to you, it's going to be more difficult to troubleshoot. It's kind of like learning, get command line versus like get cracking or something. Once, once you get in a bind, if you don't understand the underlying steps, it's really hard to get yourself out of a bind, versus if you understand what the plugin or the app is doing, then, um, you can get out of situations a lot easier. That's a good place. That's, that's where I'd start. >>Yeah. Thank you. Um, Camden asks better to build test environment images, every commit in CII. So this is like one of those opinions of we're all gonna have some different, uh, or build on build images on every commit, leveraging the cash, or build them once outside the test pile pipeline. Um, what say you people? >>Uh, well, I I've seen both and generally speaking, my preference is, um, I guess the ant, the it's a consultant answer, right? I think it depends on what you're trying to do, right. So if you have a lot of small changes that are being made and you're creating images for each of those commits, you're going to have a lot of images in your, in your registry, right? And on top of that, if you're building those images, uh, through CAI frequently, if you're using Docker hub or something like that, you might run into rate limiting issues because of Docker's new rate, limiting, uh, rate limits that they put in place. Um, but that might be beneficial if the, if being able to roll back between those small changes while you're testing is important to you. Uh, however, if all you care about is being able to use Docker images, um, or being able to correlate versions to your Docker images, or if you're the type of team that doesn't even use him, uh, does he even use, uh, virgins in your image tags? Then I would think that that might be a little, much you might want to just have in your CIO. You might want to have a stage that builds your Docker images and Docker image and pushes it into your registry, being done first particular branches instead of having to be done on every commit regardless of branch. But again, it really depends on the team. It really depends on what you're building. It really depends on your workflow. It can depend on a number of things like a curse sometimes too. Yeah. Yeah. >>Once had two points here, you know, I've seen, you know, the pattern has been at every, with every, uh, uh, commit, assuming that you have the right set of tests that would kind of, uh, you would benefit from actually seeing, um, the, the, the, the testing workflow go through and can detect any issue within, within the build or whatever you're trying to test against. But if you're just a building without the appropriate set of tests, then you're just basically consuming almond, adding time, as well as all the, the image, uh, stories associated with it without treaty reaping the benefit of, of, of this pattern. Uh, and the second point is, again, I think if you're, if you're going to end up doing a per commit, uh, definitely recommend having some type of, uh, uh, image purging, um, uh, and, and, and garbage collection process to ensure that you're not just wasting, um, all the stories needed and also, um, uh, optimizing your, your bill process, because that will end up being the most time-consuming, um, um, you know, within, within your pipeline. So this is my 2 cents on this. >>Yeah, that's good stuff. I mean, those are both of those are conversations that could lead us into the rabbit hole for the rest of the day on storage management, uh, you know, CP CPU minutes for, uh, you know, your build stuff. I mean, if you're in any size team, more than one or two people, you immediately run into headaches with cost of CIA, because we have now the problem of tools, right? We have so many tools. We can have the CIS system burning CPU cycles all day, every day, if we really wanted to. And so you re very quickly, I think, especially if you're on every commit on every branch, like that gets you into a world of cost mitigation, and you probably are going to have to settle somewhere in the middle on, uh, between the budget, people that are saying you're spending way too much money on the CII platform, uh, because of all these CPU cycles, and then the developers who would love to have everything now, you know, as fast as possible and the biggest, biggest CPU's, and the biggest servers, and have the bills, because the bills can never go fast enough, right. >>There's no end to optimizing your build workflow. Um, we have another question on that. This is another topic that we'll all probably have different takes on is, uh, basically, uh, version tags, right? So on images, we, we have a very established workflow in get for how we make commits. We have commit shots. We have, uh, you know, we know get tags and there's all these things there. And then we go into images and it's just this whole new world that's opened up. Like there's no real consensus. Um, so what, what are your thoughts on the strategy for teams in their image tag? Again, another, another culture thing. Um, commander, >>I mean, I'm a fan of silver when we have no other option. Um, it's just clean and I like the timestamp, you know, exactly when it was built. Um, I don't really see any reason to use another, uh, there's just normal, incremental, um, you know, numbering, but I love the fact that you can pull any tag and know exactly when it was created. So I'm a big fan of bar, if you can make that work for your organization. >>Yep. People are mentioned that in chat, >>So I like as well. Uh, I'm a big fan of it. I think it's easy to be able to just be as easy to be able to signify what a major changes versus a minor change versus just a hot fix or, you know, some or some kind of a bad fix. The problem that I've found with having teams adopt San Bernardo becomes answering these questions and being able to really define what is a major change, what is a minor change? What is a patch, right? And this becomes a bit of an overhead or not so much of an overhead, but, uh, uh, uh, a large concern for teams who have never done versioning before, or they never been responsible for their own versioning. Um, in fact, you know, I'm running into that right now, uh, with, with a client that I'm working with, where a lot, I'm working with a lot of teams, helping them move their applications from a legacy production environment into a new one. >>And in doing so, uh, versioning comes up because Docker images, uh, have tags and usually the tax correlate to versions, but some teams over there, some teams that I'm working with are only maintaining a script and others are maintaining a fully fledged JAK, three tier application, you know, with lots of dependencies. So telling the script, telling the team that maintains a script, Hey, you know, you should use somber and you should start thinking about, you know, what's major, what's my number what's patch. That might be a lot for them. And for someone or a team like that, I might just suggest using commit shots as your versions until you figure that out, or maybe using, um, dates as your version, but for the more for the team, with the larger application, they probably already know the answers to those questions. In which case they're either already using Sember or they, um, or they may be using some other version of the strategy and might be in December, might suit them better. So, um, you're going to hear me say, it depends a lot, and I'm just going to say here, it depends. Cause it really does. Carlos. >>I think you hit on something interesting beyond just how to version, but, um, when to consider it a major release and who makes those decisions, and if you leave it to engineers to version, you're kind of pushing business decisions down the pipe. Um, I think when it's a minor or a major should be a business decision and someone else needs to make that call someone closer to the business should be making that call as to when we want to call it major. >>That's a really good point. And I add some, I actually agree. Um, I absolutely agree with that. And again, it really depends on the team that on the team and the scope of it, it depends on the scope that they're maintaining, right? And so it's a business application. Of course, you're going to have a product manager and you're going to have, you're going to have a product manager who's going to want to make that call because that version is going to be out in marketing. People are going to use it. They're going to refer to and support calls. They're going to need to make those decisions. Sember again, works really, really well for that. Um, but for a team that's maintaining the scripts, you know, I don't know, having them say, okay, you must tell me what a major version is. It's >>A lot, but >>If they want it to use some birds great too, which is why I think going back to what you originally said, Sember in the absence of other options. I think that's a good strategy. >>Yeah. There's a, there's a, um, catching up on chat. I'm not sure if I'm ever going to catch up, but there's a lot of people commenting on their favorite CII systems and it's, and it, it just goes to show for the, the testing and deployment community. Like how many tools there are out there, how many tools there are to support the tools that you're using. Like, uh, it can be a crazy wilderness. And I think that's, that's part of the art of it, uh, is that these things are allowing us to build our workflows to the team's culture. Um, and, uh, but I do think that, you know, getting into like maybe what we hope to be at what's next is I do hope that we get to, to try to figure out some of these harder problems of consistency. Uh, one of the things that led me to Docker at the beginning to begin with was the fact that it wa it created a consistent packaging solution for me to get my code, you know, off of, off of my site of my local system, really, and into the server. >>And that whole workflow would at least the thing that I was making at each step was going to be the same thing used. Right. And that, that was huge. Uh, it was also, it also took us a long time to get there. Right. We all had to, like Docker was one of those ones that decade kind of ideas of let's solidify the, enter, get the consensus of the community around this idea. And we, and it's not perfect. Uh, you know, the Docker Docker file is not the most perfect way to describe how to make your app, but it is there and we're all using it. And now I'm looking for that next piece, right. Then hopefully the next step in that, um, that where we can all arrive at a consensus so that once you hop teams, you know, okay. We all knew Docker. We now, now we're all starting to get to know the manifests, but then there's this big gap in the middle where it's like, it might be one of a dozen things. Um, you know, so >>Yeah, yeah. To that, to that, Brett, um, you know, uh, just maybe more of a shameless plug here and wanting to kind of talk about one of the things that I'm on. So excited, but I work, I work at Tasha Corp. I don't know anyone, or I don't know if many people have heard of, um, you know, we tend to focus a lot on workflows versus technologies, right. Because, you know, as you can see, even just looking at the chat, there's, you know, ton of opinions on the different tooling, right. And, uh, imagine having, you know, I'm working with clients that have 10,000 developers. So imagine taking the folks in the chat and being partnered with one organization or one company and having to make decisions on how to build software. Um, but there's no way you can conversion one or, or one way or one tool, uh, and that's where we're facing in the industry. >>So one of the things that, uh, I'm pretty excited about, and I don't know if it's getting as much traction as you know, we've been focused on it. This is way point, which is a project, an open source project. I believe we got at least, uh, last year, um, which is, it's more of, uh, it's, it is aim to address that really, uh, uh, Brad set on, you know, to come to tool to, uh, make it extremely easy and simple. And, you know, to describe how you want to build, uh, deploy or release your application, uh, in, in a consistent way, regardless of the tools. So similar to how you can think of Terraform and having that pluggability to say Terraform apply or plan against any cloud infrastructure, uh, without really having to know exactly the details of how to do it, uh, this is what wave one is doing. Um, and it can be applied with, you know, for the CIA, uh, framework. So, you know, task plugability into, uh, you know, circle CEI tests to Docker helm, uh, Kubernetes. So that's the, you know, it's, it's a hard problem to solve, but, um, I'm hopeful that that's the path that we're, you know, we'll, we'll eventually get to. So, um, hope, you know, you can, you can, uh, see some of the, you know, information, data on it, on, on HashiCorp site, but I mean, I'm personally excited about it. >>Yeah. Uh I'm to gonna have to check that out. And, um, I told you on my live show, man, we'll talk about it, but talk about it for a whole hour. Uh, so there's another question here around, uh, this, this is actually a little bit more detailed, but it is one that I think a lot of people deal with and I deal with a lot too, is essentially the question is from Cameron, uh, D essentially, do you use compose in your CIO or not Docker compose? Uh, because yes I do. Yeah. Cause it, it, it, it solves so many problems am and not every CGI can, I don't know, there's some problems with a CIO is trying to do it for me. So there are pros and cons and I feel like I'm still on the fence about it because I use it all the time, but also it's not perfect. It's not always meant for CIA. And CIA sometimes tries to do things for you, like starting things up before you start other parts and having that whole order, uh, ordering problem of things anyway. W thoughts and when have thoughts. >>Yes. I love compose. It's one of my favorite tools of all time. Um, and the reason why it's, because what I often find I'm working with teams trying to actually let me walk that back, because Jack on the chat asked a really interesting question about what, what, what the hardest thing about CIS for a lot of teams. And in my experience, the hardest thing is getting teams to build an app that is the same app as what's built in production. A lot of CGI does things that are totally different than what you would do in your local, in your local dev. And as a result of that, you get, you got this application that either doesn't work locally, or it does work, but it's a completely different animal than what you would get in production. Right? So what I've found in trying to get teams to bridge that gap by basically taking their CGI, shifting the CII left, I hate the shift left turn, but I'll use it. >>I'm shifting the CIO left to your local development is trying to say, okay, how do we build an app? How do we, how do we build mot dependencies of that app so that we can build so that we can test our app? How do we run tests, right? How do we build, how do we get test data? And what I found is that trying to get teams to do all this in Docker, which is normally a first for a lot of teams that I'm working with, trying to get them all to do all of this. And Docker means you're running Docker, build a lot running Docker, run a lot. You're running Docker, RM a lot. You ran a lot of Docker, disparate Docker commands. And then on top of that, trying to bridge all of those containers together into a single network can be challenging without compose. >>So I like using a, to be able to really easily categorize and compartmentalize a lot of the things that are going to be done in CII, like building a Docker image, running tests, which is you're, you're going to do it in CII anyway. So running tests, building the image, pushing it to the registry. Well, I wouldn't say pushing it to the registry, but doing all the things that you would do in local dev, but in the same network that you might have a mock database or a mock S3 instance or some of something else. Um, so it's just easy to take all those Docker compose commands and move them into your Yammel file using the hub actions or your dankest Bob using Jenkins, or what have you. Right. It's really, it's really portable that way, but it doesn't work for every team. You know, for example, if you're just a team that, you know, going back to my script example, if it's a really simple script that does one thing on a somewhat routine basis, then that might be a lot of overhead. Um, in that case, you know, you can get away with just Docker commands. It's not a big deal, but the way I looked at it is if I'm, if I'm building, if I build something that's similar to a make bile or rate file, or what have you, then I'm probably gonna want to use Docker compose. If I'm working with Docker, that's, that's a philosophy of values, right? >>So I'm also a fan of Docker compose. And, um, you know, to your point, Carlos, the whole, I mean, I'm also a fan of shifting CEI lift and testing lift, but if you put all that logic in your CTI, um, it changes the L the local development experience from the CGI experience. Versus if you put everything in a compose file so that what you build locally is the same as what you build in CGI. Um, you're going to have a better experience because you're going to be testing something more, that's closer to what you're going to be releasing. And it's also very easy to look at a compose file and kind of, um, understand what the dependencies are and what's happening is very readable. And once you move that stuff to CGI, I think a lot of developers, you know, they're going to be intimidated by the CGI, um, whatever the scripting language is, it's going to be something they're going to have to wrap their head around. >>Um, but they're not gonna be able to use it locally. You're going to have to have another local solution. So I love the idea of a composed file use locally, um, especially if he can Mount the local workspace so that they can do real time development and see their changes in the exact same way as it's going to be built and tested in CGI. It gives developers a high level of confidence. And then, you know, you're less likely to have issues because of discrepancies between how it was built in your local test environment versus how it's built in NCI. And so Docker compose really lets you do all of that in a way that makes your solution more portable, portable between local dev and CGI and reduces the number of CGI cycles to get, you know, the test, the test data that you need. So that's why I like it for really, for local dev. >>It'll be interesting. Um, I don't know if you all were able to see the keynote, but there was a, there was a little bit, not a whole lot, but a little bit talk of the Docker, compose V two, which has now built into the Docker command line. And so now we're shifting from the Python built compose, which was a separate package. You could that one of the challenges was getting it into your CA solution because if you don't have PIP and you got down on the binary and the binary wasn't available for every platform and, uh, it was a PI installer. It gets a little nerdy into how that works, but, uh, and the team is now getting, be able to get unified with it. Now that it's in Golang and it's, and it's plugged right into the Docker command line, it hopefully will be easier to distribute, easier to, to use. >>And you won't have to necessarily have dependencies inside of where you're running it because there'll be a statically compiled binary. Um, so I've been playing with that, uh, this year. And so like training myself to do Docker going from Docker dash compose to Docker space, compose. It is a thing I I'm almost to the point of having to write a shell replacement. Yeah. Alias that thing. Um, but, um, I'm excited to see what that's going, cause there's already new features in it. And it, these built kit by default, like there's all these things. And I, I love build kit. We could make a whole session on build kit. Um, in fact there's actually, um, maybe going on right now, or right around this time, there is a session on, uh, from Solomon hikes, the seat, uh, co-founder of Docker, former CTO, uh, on build kit using, uh, using some other tool on top of build kit or whatever. >>So that, that would be interesting for those of you that are not watching that one. Cause you're here, uh, to do a check that one out later. Um, all right. So another good question was caching. So another one, another area where there is no wrong answers probably, and everyone has a different story. So the question is, what are your thoughts on CII build caching? There's often a debate between security. This is from Quentin. Thank you for this great question. There's often a debate between security reproducibility and build speeds. I haven't found a good answer so far. I will just throw my hat in the ring and say that the more times you want to build, like if you're trying to build every commit or every commit, if you're building many times a day, the more caching you need. So like the more times you're building, the more caching you're gonna likely want. And in most cases caching doesn't bite you in the butt, but that could be, yeah, we, can we get the bit about that? So, yeah. Yeah. >>I'm going to quote Carlos again and say, it depends on, on, you know, how you're talking, you know, what you're trying to build and I'm quoting your colors. Um, yeah, it's, it's got, it's gonna depend because, you know, there are some instances where you definitely want to use, you know, depends on the frequency that you're building and how you're building. Um, it's you would want to actually take advantage of cashing functionalities, um, for the build, uh, itself. Um, but if, um, you know, as you mentioned, there could be some instances where you would want to disable, um, any caching because you actually want to either pull a new packages or, um, you know, there could be some security, um, uh, disadvantages related to security aspects that would, you know, you know, using a cache version of, uh, image layer, for example, could be a problem. And you, you know, if you have a fleet of build, uh, engines, you don't have a good grasp of where they're being cashed. We would have to, um, disable caching in that, in that, um, in those instances. So it, it would depend. >>Yeah, it's, it's funny you have that problem on both sides of cashing. Like there are things that, especially in Docker world, they will cash automatically. And, and then, and then you maybe don't realize that some of that caching could be bad. It's, it's actually using old, uh, old assets, old artifacts, and then there's times where you would expect it to cash, that it doesn't cash. And then you have to do something extra to enable that caching, especially when you're dealing with that cluster of, of CIS servers. Right. And the cloud, the whole clustering problem with caching is even more complex, but yeah, >>But that's, that's when, >>Uh, you know, ever since I asked you to start using build kits and able to build kit, you know, between it's it's it's reader of Boston in, in detecting word, you know, where in, in the bill process needs to cash, as well as, uh, the, the, um, you know, the process. I don't think I've seen any other, uh, approach there that comes close to how efficient, uh, that process can become how much time it can actually save. Uh, but again, I think, I think that's, for me that had been my default approach, unless I actually need something that I would intentionally to disable caching for that purpose, but the benefits, at least for me, the benefits of, um, how bill kit actually been processing my bills, um, from the builds as well as, you know, using the cash up until, you know, how it detects the, the difference in, in, in the assets within the Docker file had been, um, you know, uh, pretty, you know, outweigh the disadvantages that it brings in. So it, you know, take it each case by case. And based on that, determine if you want to use it, but definitely recommend those enabling >>In the absence of a reason not to, um, I definitely think that it's a good approach in terms of speed. Um, yeah, I say you cash until you have a good reason not to personally >>Catch by default. There you go. I think you catch by default. Yeah. Yeah. And, uh, the trick is, well, one, it's not always enabled by default, especially when you're talking about cross server. So that's a, that's a complexity for your SIS admins, or if you're on the cloud, you know, it's usually just an option. Um, I think it also is this, this veers into a little bit of, uh, the more you cash the in a lot of cases with Docker, like the, from like, if you're from images and checked every single time, if you're not pinning every single thing, if you're not painting your app version, you're at your MPN versions to the exact lock file definition. Like there's a lot of these things where I'm I get, I get sort of, I get very grouchy with teams that sort of let it, just let it all be like, yeah, we'll just build two images and they're totally going to have different dependencies because someone happened to update that thing and after whatever or MPM or, or, and so I get grouchy about that, cause I want to lock it all down, but I also know that that's going to create administrative burden. >>Like the team is now going to have to manage versions in a very much more granular way. Like, do we need to version two? Do we need to care about curl? You know, all that stuff. Um, so that's, that's kind of tricky, but when you get to, when you get to certain version problems, uh, sorry, uh, cashing problems, you, you, you don't want those set those caches to happen because it, if you're from image changes and you're not constantly checking for a new image, and if you're not pinning that V that version, then now you, you don't know whether you're getting the latest version of Davion or whatever. Um, so I think that there's, there's an art form to the more you pen, the less you have, the less, you have to be worried about things changing, but the more you pen, the, uh, all your versions of everything all the way down the stack, the more administrative stuff, because you're gonna have to manually change every one of those. >>So I think it's a balancing act for teams. And as you mature, I to find teams, they tend to pin more until they get to a point of being more comfortable with their testing. So the other side of this argument is if you trust your testing, then you, and you have better testing to me, the less likely to the subtle little differences in versions have to be penned because you can get away with those minor or patch level version changes. If you're thoroughly testing your app, because you're trusting your testing. And this gets us into a whole nother rant, but, uh, yeah, but talking >>About penny versions, if you've got a lot of dependencies isn't that when you would want to use the cash the most and not have to rebuild all those layers. Yeah. >>But if you're not, but if you're not painting to the exact patch version and you are caching, then you're not technically getting the latest versions because it's not checking for all the time. It's a weird, there's a lot of this subtle nuance that people don't realize until it's a problem. And that's part of the, the tricky part of allow this stuff, is it, sometimes the Docker can be almost so much magic out of the box that you, you, you get this all and it all works. And then day two happens and you built it a second time and you've got a new version of open SSL in there and suddenly it doesn't work. Um, so anyway, uh, that was a great question. I've done the question on this, on, uh, from heavy. What do you put, where do you put testing in your pipeline? Like, so testing the code cause there's lots of types of testing, uh, because this pipeline gets longer and longer and Docker building images as part of it. And so he says, um, before staging or after staging, but before production, where do you put it? >>Oh man. Okay. So, um, my, my main thought on this is, and of course this is kind of religious flame bait, so sure. You know, people are going to go into the compensation wrong. Carlos, the boy is how I like to think about it. So pretty much in every stage or every environment that you're going to be deploying your app into, or that your application is going to touch. My idea is that there should be a build of a Docker image that has all your applications coded in, along with its dependencies, there's testing that tests your application, and then there's a deployment that happens into whatever infrastructure there is. Right. So the testing, they can get tricky though. And the type of testing you do, I think depends on the environment that you're in. So if you're, let's say for example, your team and you have, you have a main branch and then you have feature branches that merged into the main branch. >>You don't have like a pre-production branch or anything like that. So in those feature branches, whenever I'm doing CGI that way, I know when I freak, when I cut my poll request, that I'm going to merge into main and everything's going to work in my feature branches, I'm going to want to probably just run unit tests and maybe some component tests, which really, which are just, you know, testing that your app can talk to another component or another part, another dependency, like maybe a database doing tests like that, that don't take a lot of time that are fascinating and right. A lot of would be done at the beach branch level and in my opinion, but when you're going to merge that beach branch into main, as part of a release in that activity, you're going to want to be able to do an integration tasks, to make sure that your app can actually talk to all the other dependencies that it talked to. >>You're going to want to do an end to end test or a smoke test, just to make sure that, you know, someone that actually touches the application, if it's like a website can actually use the website as intended and it meets the business cases and all that, and you might even have testing like performance testing, low performance load testing, or security testing, compliance testing that would want to happen in my opinion, when you're about to go into production with a release, because those are gonna take a long time. Those are very expensive. You're going to have to cut new infrastructure, run those tests, and it can become quite arduous. And you're not going to want to run those all the time. You'll have the resources, uh, builds will be slower. Uh, release will be slower. It will just become a mess. So I would want to save those for when I'm about to go into production. Instead of doing those every time I make a commit or every time I'm merging a feature ranch into a non main branch, that's the way I look at it, but everything does a different, um, there's other philosophies around it. Yeah. >>Well, I don't disagree with your build test deploy. I think if you're going to deploy the code, it needs to be tested. Um, at some level, I mean less the same. You've got, I hate the term smoke tests, cause it gives a false sense of security, but you have some mental minimum minimal amount of tests. And I would expect the developer on the feature branch to add new tests that tested that feature. And that would be part of the PR why those tests would need to pass before you can merge it, merge it to master. So I agree that there are tests that you, you want to run at different stages, but the earlier you can run the test before going to production. Um, the fewer issues you have, the easier it is to troubleshoot it. And I kind of agree with what you said, Carlos, about the longer running tests like performance tests and things like that, waiting to the end. >>The only problem is when you wait until the end to run those performance tests, you kind of end up deploying with whatever performance you have. It's, it's almost just an information gathering. So if you don't run your performance test early on, um, and I don't want to go down a rabbit hole, but performance tests can be really useless if you don't have a goal where it's just information gap, uh, this is, this is the performance. Well, what did you expect it to be? Is it good? Is it bad? They can get really nebulous. So if performance is really important, um, you you're gonna need to come up with some expectations, preferably, you know, set up the business level, like what our SLA is, what our response times and have something to shoot for. And then before you're getting to production. If you have targets, you can test before staging and you can tweak the code before staging and move that performance initiative. Sorry, Carlos, a little to the left. Um, but if you don't have a performance targets, then it's just a check box. So those are my thoughts. I like to test before every deployment. Right? >>Yeah. And you know what, I'm glad that you, I'm glad that you brought, I'm glad that you brought up Escalades and performance because, and you know, the definition of performance says to me, because one of the things that I've seen when I work with teams is that oftentimes another team runs a P and L tests and they ended, and the development team doesn't really have too much insight into what's going on there. And usually when I go to the performance team and say, Hey, how do you run your performance test? It's usually just a generic solution for every single application that they support, which may or may not be applicable to the application team that I'm working with specifically. So I think it's a good, I'm not going to dig into it. I'm not going to dig into the rabbit hole SRE, but it is a good bridge into SRE when you start trying to define what does reliability mean, right? >>Because the reason why you test performance, it's test reliability to make sure that when you cut that release, that customers would go to your site or use your application. Aren't going to see regressions in performance and are not going to either go to another website or, you know, lodge in SLA violation or something like that. Um, it does, it does bridge really well with defining reliability and what SRE means. And when you have, when you start talking about that, that's when you started talking about how often do I run? How often do I test my reliability, the reliability of my application, right? Like, do I have nightly tasks in CGI that ensure that my main branch or, you know, some important branch I does not mean is meeting SLA is meeting SLR. So service level objectives, um, or, you know, do I run tasks that ensure that my SLA is being met in production? >>Like whenever, like do I use, do I do things like game days where I test, Hey, if I turn something off or, you know, if I deploy this small broken code to production and like what happens to my performance? What happens to my security and compliance? Um, you can, that you can go really deep into and take creating, um, into creating really robust tests that cover a lot of different domains. But I liked just using build test deploy is the overall answer to that because I find that you're going to have to build your application first. You're going to have to test it out there and build it, and then you're going to want to deploy it after you test it. And that order generally ensures that you're releasing software. That works. >>Right. Right. Um, I was going to ask one last question. Um, it's going to have to be like a sentence answer though, for each one of you. Uh, this is, uh, do you lint? And if you lint, do you lent all the things, if you do, do you fail the linters during your testing? Yes or no? I think it's going to depend on the culture. I really do. Sorry about it. If we >>Have a, you know, a hook, uh, you know, on the get commit, then theoretically the developer can't get code there without running Melinta anyway, >>So, right, right. True. Anyone else? Anyone thoughts on that? Linting >>Nice. I saw an additional question online thing. And in the chat, if you would introduce it in a multi-stage build, um, you know, I was wondering also what others think about that, like typically I've seen, you know, with multi-stage it's the most common use case is just to produce the final, like to minimize the, the, the, the, the, the image size and produce a final, you know, thin, uh, layout or thin, uh, image. Uh, so if it's not for that, like, I, I don't, I haven't seen a lot of, you know, um, teams or individuals who are actually within a multi-stage build. There's nothing really against that, but they think the number one purpose of doing multi-stage had been just producing the minimalist image. Um, so just wanted to kind of combine those two answers in one, uh, for sure. >>Yeah, yeah, sure. Um, and with that, um, thank you all for the great questions. We are going to have to wrap this up and we could go for another hour if we all had the time. And if Dr. Khan was a 24 hour long event and it didn't sadly, it's not. So we've got to make room for the next live panel, which will be Peter coming on and talking about security with some developer ex security experts. And I wanted to thank again, thank you all three of you for being here real quick, go around the room. Um, uh, where can people reach out to you? I am, uh, at Bret Fisher on Twitter. You can find me there. Carlos. >>I'm at dev Mandy with a Y D E N D Y that's me, um, >>Easiest name ever on Twitter, Carlos and DFW on LinkedIn. And I also have a LinkedIn learning course. So if you check me out on my LinkedIn learning, >>Yeah. I'm at Nicola Quebec. Um, one word, I'll put it in the chat as well on, on LinkedIn, as well as, uh, uh, as well as Twitter. Thanks for having us, Brett. Yeah. Thanks for being here. >>Um, and, and you all stay around. So if you're in the room with us chatting, you're gonna, you're gonna, if you want to go to see the next live panel, I've got to go back to the beginning and do that whole thing, uh, and find the next, because this one will end, but we'll still be in chat for a few minutes. I think the chat keeps going. I don't actually know. I haven't tried it yet. So we'll find out here in a minute. Um, but thanks you all for being here, I will be back a little bit later, but, uh, coming up next on the live stuff is Peter Wood security. Ciao. Bye.

>>Hello, uh, welcome to everyone. Uh, my name is Carlos lights. I'm a HP managing director, Portugal. Um, I have the privilege today where we make Hable, which is the business to business director for Altis Portugal. Uh, Paul, thank you very much. I want to start by faith you for your presence and availability to be with us today and to be able to share a few words about our partnership in particular use of our it consumption model, uh, as a service. So HP ring like, Oh, uh, maybe it's a good, uh, is a good way to start explaining a little bit about Altis right out. This is a multinational organization. Uh, you have presence in four countries, France, Portugal, Israel, and Dominica in a week, but today we will focus on, uh, on Portugal. Right. So could you please polo, uh, tell us about the Altis Portugal and, uh, your company's vision follow. >>Okay. Hello, Carlos, thank you very much for the invitation and regarding your question. Well, all this protocol was born as a telephone operator and after a few decades of transformation, namely on the B2B markets is now the leading Portuguese player in ICT. We have positioned ourselves as the preferred partner for digital transformation of organizations, both private and public. We have a large portfolio of an ICT portfolio of products and customized solutions from IOT, BPO, security payments, and many other areas. And we are committed to achieve a more competitive, collaborative, digital, and low carbon economy through our in-house products and our partner solutions like HBA, uh, regarding cloud. We have a decade of cloud presence in the market, uh, based on five data centers network that we have here in Portugal that are operating since 1999. Uh, so we have made this cloud journey with a strong focus on managing value added services, serving both in years and also top organizations. Uh, we started to believe that as a country, we cannot achieve the level of modernization and automation of operational models, or even the relation of company's cost structure without the throng and widespread adoption of a model as a set of display, as you go a lot of liken services, >>Paul, um, you know, um, as well, companies are currently, uh, facing many challenge, uh, and looking for solutions that address to their needs, but also, uh, should be future proof. Right? So how do you see everybody's cloud role on the elping, uh, achieve business goals and requirements? >>Well, uh, cloud approach is, are becoming a priority on organizations and customers are increasingly aware of the, of their solutions to their needs. So what coffee has shown us is how important is the agility flexibility, and thank to markets we all needed to bring, as far as you could, new solutions to the market, increase digital means and accomplished the use new workplace information because we are all working now, I'm in the office, I mean at house. And so what we see here in Portugal is that cloud enabled organizations were better prepared to this challenge and prior management visitations within companies, I think that will probably be now overcome by new decision-makers and some, so some verticals like healthcare banking got public administration, they have tight retirements, tight requirements on compliance that the security control. And I think that these are some of the verticals where only in a nightmarish cloud approach we'll compare unite with all these called VC cities. >>And so I think that under the present situation, budgets constraints, efficiency, and predictability are even more present at decision-making process. And I don't think that traditional it models will no longer cope with this challenge. And so therefore, uh, every thought strategy are presently delivering customers the best of both worlds. They want simplified it provisioning and operational LSD city, but never, but then nevertheless are compelled to keep that close to home when that the protection is critical and compliance, and that the governments are to shop every cloud service services supported by the right partners tools like GreenLake central. I think that will be the key to achieve a single as a service experience from edge to plow. They allow the security and of control of a non-brand model, along with the corporate agility and the OPEX flexibility so much demanded by our clients. >>So BOLO, uh, regarding the experience, uh, fortunately we have a ready common contracts and common customers, right? And the, well, from your experience, uh, what do you think are the benefits customers are getting from each beaming? Like, can you share some examples use cases? >>Okay. Of course. Well, uh, as I said before, HBA brings to the table a key differentiator, which is the GreenLake central. We have one portal to manage all cloud on prem having at the same time, the control, the easy to use and better capacity to plan future needs. So it serves both it finance and legal teams giving the proper visibility to all of these stakeholders. In terms of examples, we have customers that are using GreenLake to suppose, suppose private and public sector. We have implementations both at Altis and customers' premises that the centers on a very diverse type of business apps from mission critical help, sometimes legacy to SAP or some cloud native apps. For instance, we have solutions of stars as a service for the energy sector on the transport sector and a data center as a service on public administration on customers that need to keep the it on prem because of compliance, but look for the cloud flexibility and the model as a service. >>Good. I see, uh, I see in fact, uh, a positive, a very positive future, uh, in our partnership, but, uh, w what is your opinion about that, uh, uh, how you see the partnership with HP and, uh, and the position to, to give you the ability to even have more success? >>Well, I'm putting this question, talking a little bit about the near future. I think that the short to medium term, the future, uh, will be concentrating on solving possible endemic crisis and sell both customers and ourselves service providers. Uh, we faced this challenge and I think that we were able to cope with these challenges on 2021. And we are very supported on technology and digital solutions. And I think that with the help of HPA, we are being able to, to pass this reality check, uh, and under such critical circumstances. But now we need to step forward. We need to leverage the increase e-comm levels. Remote work breath is a way of in there. So we need to go towards the digital transformation of organizations. We need to extend the infrastructure to where business happens. Uh, we have to infuse cloud closer to the network. We are telco allowing for a new class of cloud native and innovative applications for customers. >>Some verticals where LTC is aiming for a new position, also urge for closer collaboration with called partners like HPA, for instance, e-health will bring us new opportunities in the near future. And the proper ICT foundations are crucial for the new health care services, patient focused approaches and that the driven decisions and being cloud markets made sure. Now, I think that some companies still count on their partners to facilitate the cloud journey, helping them underline the business cases on cloud users apart consulting skills to facilitate change, bring peers use cases that can easily apply NHPA is the most value for out. It says much to bring the new cloud technology, but also the existing experience, the use casing, the use cases and helping us go through the learning curve in other markets. So, uh, for the future, we hope for the best, and we will count on a leading partner like HPA to drivers too, >>On these uncertain, but promising future. Good. I want to reinforce the, thank you very much for your shares. Share this content, this information with us. Uh, it was a real, it is a real pleasure to work with you. Uh, it's a pleasure for all your work without these as a company, but as well, personally, we, your follow and the, because I think it's important to wherever address relationship, and we have now the trust relationship with the organization and with personally as well. So thank you very much, Paulo, and have a nice day. Thank you. Thank you. Thank you very much..

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to you by Dell Technologies. Hello, everyone. And welcome back to the cubes wall to wall coverage of Dell Technologies World, The digital experience 2020. The Virtual Cube is coming at you. I'm Dave Volonte. And with me or two Great guest, my colleague and longtime business friend Howard Elias. He's the chief customer officer and president of services and digital Adele. And also joining me is Sri Raj, aka Sri Can't him Nene, who is the managing director of digital insights at Cargo, which is one of the world's largest privately held companies in the top maker and distributor of agricultural products and the things that we eat every day. Gentlemen, thanks so much for your time and coming on the Cube. Great to see you. >>Great to see you, Dave. And three. Great to see you again as well. >>Good to be with you both. So >>I wanna Howard, I wanna talk about start by talking about digital transformation. I'm gonna make it laugh. So I was talking to a customer every day or the other day, and we all talk about, you know, digital transformation. And I said, What's digital transformation to you? He said, Dave, my S a P system was 15 years old and I have to upgrade. It was like, Okay, there's eso There's a spectrum, as you know, but what do you seeing as digital transformation? What does that mean to your customers? >>Well, what we're seeing is a glimpse of the future. And first of all, Dave, Great to be with you again, uh, free and all of you out there hope everybody's safe. And, well, thanks for joining us, Adele Technologies World today. But digital transformation from our customers perspectives the technology enablement of experiences with customers, partners and employees, a swells automating processes to deliver value to the all key stakeholders. And we've just seen a glimpse of the future. Customers are accelerating their adoption of technology. We see this through necessity, right when everybody had to pivot from or toe work from home, especially those professional workers and for the most part, whether companies plan forward or not, we all embraced and learned new ways of being productive remotely, and that was all enabled by technology. But we've seen it in every walk of life. It's really an acceleration of trends that were already underway, whether it was the remote experience for professional employees, whether it's e commerce experience, whether it's telemedicine, distance learning. All of these things have been available for a while, but we've seen them be embraced and accelerated tremendously due to what we've seen over the last six months in all industries. And free will talk about what's happening specifically in the agricultural industry, and what we've seen is customers that have made investments over the years have been ableto move even faster in their specific industries. We've just on a survey of about 4600 customers around the world, and 80% have accelerated their investments in digital technology to improve the experience of their employees of their customers and of their partners. >>Yes, so So thank you for that, Howard. Three. I mean, a lot of people might think of cargo. There's physical business, but it's anything but. I mean, you've got such a huge data component to your business, but I wonder what you would add. I mean, we're maybe talk a little bit. I mean, it's such amazingly, you know, rich and deep company. But maybe talk about your digital transformation journey and at least in your sphere of the world where you're at. >>Yeah, thanks, David. You know, Howard's absolutely right. What? What Cove it has done is just accelerated the need for technology on farm and with our customers. And and certainly in the last few months, we've seen that accelerate tremendously, right? A t end of the day. Agriculture has been a technology first, um, industry for for hundreds of years, and and so we're seeing that take fold in the form of digital adoption, the use of analytics, the use of really unique sensor technologies like cameras and computer vision. Um, sound I liken it to the senses that we all have every day that we used to make decisions. Well, we're now seeing that adopted with our with our customers. And so it's a really interesting time, and I think an opportunity for for the industry to really move forward. >>I mean, in terms of the three in terms of the pandemic, you know, we we talked to a lot of customers. Howard just mentioned a survey. You certainly saw the pivot in tow work from home you know, increase in laptop momentum. And in Dell's business, we saw that you're seeing identity access, management, cloud security and point security. Even even VD I These were big tail winds early on. What did the pandemic due to your business and just in terms of your your priorities did you have to obviously shift to those things to support work from home? What happened to your digital transformation was was anything put on hold and is restarting. Can you just Yeah, I don't know what you could tell us about that, but anything you could describe and add some color to that narrative would be really helpful to our audience. >>Certainly. Yeah. You know, I think overnight we had, ah, workforce that went from being in the office toe working from home and and that just accelerated the need for for collaboration tools. Things like like teams and and Skype and Zoom have just taken off right? But also technologies that allow for virtual engagement, like white boarding and brainstorming sessions that we used to do in the office with customers and suppliers. We're now having to do in a virtual setting. So so that has just transformed how we do business on the customer. And, you know, technologies like computer vision and and sound really transform the need to to leverage labor differently. Right? One of the biggest challenges that the cove it has has placed is how labor interacts with animals and and with food production. And we've just seen a significant adoption of technology to help alleviate some of those stresses. >>Now you guys probably have seen the tongue in cheek cartoons, the covert wrecking ball, you know, the guys in the audience or the building saying digital transformation. Not on my watch in the cove, it comes in. I've often joked, uh, I guess we have to have a sense of humor in these times, but But if it ain't broke, don't fix it. We'll cove. It kind of broke everything. And Howard, when you think about digital transformation, yes, was going on before co vid. But But there are a lot of industries that hadn't been disrupted. I think about health care. I think about financial services. I think about defense. I mean, the list goes on unlike publishing, for instance, which got totally disrupted by the Internet. But now it seems like If you're not a digital business, you're out of business. Eso Are you seeing like virtually every industry adopting digital? Or are you seeing any trends that are different by industry? What are you seeing out there >>were absolutely seeing every company in every industry adopted in their own way, thinking through their business models. I mean, even think about what's happened in your local town. How technology is able enabled restaurants to dio, you know, uh, take out and delivery through digital tools, your local dry cleaner, your your local butcher and your baker. I mean, everybody's having toe be creative and reinvent. It's not just the, you know, large professional industrial financial services companies who are also reinventing. But I go back to what I said before what we're seeing. These trends were already underway. They've just been put into hyperspeed what folks were thinking about doing in two or three years we're doing into two or three months. The pivot toe work from home worldwide happened in two or three weeks, and it's not the crisis we planned for, but we're always preparing for the future. The groundwork was laid, and now it's just been accelerated. We're seeing it everywhere, including inside Adele. You know, I think about all the processes and the way we serve our employees, our customers and partners we've accelerated were adopting the product model within our own Del digital organization, for example, that's been accelerated. The move to multi cloud on having a cloud operating model no matter where the infrastructure has been accelerated. And you know, everything we've talked about on the client experience. Security models, networking model software, defined models, every every industry, every company has had to embrace this >>so sorry. I mean, I'm fascinated by your business. I mean again, I think a lot of people think of it as a real physical business. But there's so much data. You're the head of digital insights, which is You've got data running through your your entire operations. There's other things. There's there's double take words I see in your your background like aqua culture. So So how are you re imagining the future of your industry? >>That's Ah, that's a fascinating question, Dave. You know, think, Imagine this. You could listen to a shrimp eat and then turn that into unique insights about the feeding patterns on behaviors of shrimp, right? Who would have imagined 10 years ago that we would have technology that enabled us to do things like that? Right? And so, from aquaculture thio the dairy industry to, you know, grain origination. We're leveraging digital and data to really help our customers and producers make better, more informed decisions where in in the past it was really experience that allowed them toe be good farmers and and good stewards of our planet. Now we're using technology, so it's really an opportunity toe harness, the power of digital for our industry. >>Well, you know, and it's critical because we have people to feed and actually it's working. I mean, the yields that air coming out of the industry or are amazing. I know there's a lot of discussion now, but hey, you know, we're actually getting a lot of food to people. And now there's a discussion around nutrition that's that's front and center, and I presume technology and data fit in there as well. Three. I wonder if you could comment. >>Yeah, you know, by 2050 day there will be nearly 10 billion people on this planet. And to feed that growing population, we're gonna need 70% more protein on DSO. As you think about the impacts that that that growing population has on the planet. There's also, you know, nutrition. But think about sustainability. How do we how do we grow this food and get it from the place that it's produced to the place where it's consumed in a way that's a resource efficient and effective? So there's nutrition in just the middle class in Asia, you know, having a higher propensity to spend and dealing with that challenge on one end of the spectrum and then on the other end of the spectrum, being ableto really deal with with sustainability. >>I would have watched your career over the decades, and you've had so many roles, and I always used to joke with you. They give you the hardest problems if you want. If you want to get stuff done, you give it to the busiest guy. It was always Howard, you know, help us with with our own transformations. Help us do the integrations, whether it was m and a or the course, the largest in just >>industry I love a good challenge is you know, >>I do know and so I want to get. Get the update on Dell's own transformation. I've been talking to a number of your executives this week, and it looks like you know, you guys air, drinking your own champagne, dog food and whatever you wanna call it. But but bring us up to date on what you guys are doing internally. >>We are, and we're no different than any of our customers. And having Thio focus on our digital transformation agenda, I mentioned earlier the adoption of our product model, you know, moving from a project based Dell Digital and I T Organization to one that's a product model. So these are balanced teams with a product manager, a designer and developers working closely with the business and the function in an agile manner and the C I. C. D pipeline manner. And all of this again has been accelerated. We have our own del digital cloud, which is our hybrid cloud that we leverage internally. We're software defining everything, and it's really paying dividends because what we've seen literally in the last 6 to 8 months is higher levels of security, higher levels of availability, higher levels of resiliency. We've been able to handle all of the increased transactions on our e commerce engines, all at higher quality and lower costs. Now we the groundwork for this with Jen Felch in the team over the last couple of years, but again, by necessity, had to accelerate. And we've done that. And we're even moving faster now on data pipelines and really understanding all of our key processes and understanding the work flows and the data flows, working with machine learning and artificial intelligence again, exactly the way Cargill and other of our customers are doing in their businesses. I know you're talking or have talked to Doug Schmidt. You know, we've digitized and automated thousands of processes and our services organization Theobald bility on a remote basis to service our customers were we've invented new and innovative ways the service our customers remotely versus going on site, not just in break fix, deployment, remote change, management, manage services, consulting. It's just, you know, great to see all this wonderful innovation come together serving our customers. >>Thank you for that, Howard. And you, you said something that triggered me in a good way. Data pipelines. I use that term a lot. And three I wonder if you could talk about this because you're You guys have been around since the 18 hundreds, I think the largest privately held company in United States, I think, right, and probably close to one of the largest in the world. And so >>you >>got a lot of data and a lot of different places. So a huge challenge for you is okay. How do you manage those data pipelines? Those data, the data lifecycle, And I would think the company the size of cargo to the extent that you can reduce the end to end time it takes to go from raw data to insights E. That's gonna be telephone numbers for for your business and your bottom line that you can then reinvest and get back to customers, etcetera and be competitive. I wonder if you could talk about >>you >>know, that whole concept of the data pipeline And how are you using data and and some of the challenges of compressing that end to end cycle time and Leighton >>see, to >>get to insights >>that day. You know, Carlos, 155 year old company and and at our core were a supply chain company. Right? Um, you know, taking food from where it's produced, getting it through the manufacturing process, toe customers. And so at the end of the day, I I joked that not only are we have physical supply chain company, but we're also a data supply chain company. So the data value chain right is really about taking all the different inputs in data that we have in turning that into unique insights. And I don't think there's ah company on the planet in the food space that has the ability to connect those dots in the way that we dio. And so our ability to create unique, actionable insights for our customers is going to be really powerful, especially in the in the coming years. >>So talk about let's talk about Dell a little bit. I always ask, uh, technology leaders how your vendors doing for you? How did they help you through the pandemic? How would you grade del uh, in terms of its support through the pandemic? >>Dell has been absolutely fantastic, right? I mean, I think it is really need to have partners like Dell helping us achieve our mission for our customers. And I know they feel that way about us as their customers. So it's really wonderful. Toe have the type of collaboration and partnership that we do. >>Alright, Howard, Same question for you. How would you grade Del Onda? How you guys have done through the through the pandemic with regard to supporting your customers. I mean, you're you're never one toe overhype, uh, in my experience with you. But give us the your take. >>Why would grade del by what our customers say? And we do it both through direct conversations as well as the data and telemetry we get and the data and telemetry we have in terms of our NPS r R C sat scores or service level objectives that were delivering all have remained in profile. The team has really risen to the occasion. Been super creative, passionate, full of grit. We heard Alison and Angela talk about that the Dell Technologies world this morning, and our team is embodied that spirit and that great to be able to deliver. But in the conversations we're having with customers three and his peers, uh, you know, look, it's it's been a challenging time, but as you know, Dell has always focused on delivering value for the long term. We're not in it for the short term, and that has served us well. That philosophy Theobald active. We have with working with customers, eyes always about what's in the best interests of our customers in the long term. Because if we do that, it will ultimately be in the best interest of Dell. >>Well, it's It's been amazing to just watch. I mean, it's just ironic that we got hit with this at the beginning of this decade. It's gonna It's obviously gonna define. You know what we do going forward. I think we've all talked about it. It's funny. Everybody in our business and the technology business. We've become covert experts in some way, shape or form overnight. But we've talked a lot about the the things that we see as as permanent, and I think that >>you >>know you clearly the your two companies are examples of agility leaning into technology. And, as you said, Howard here for the long term, 155 years old, I think story said so well, here's to another 155 years. Gentlemen, thanks so much for coming to Cuba. Awesome guests. >>Thanks. Day. Appreciate it. >>Thank you for watching everybody. Our continuing coverage of Dell Technologies World 2020. You're watching the Cube?

>>live from Miami, Florida It's the Q covering IBM is data in a I forum brought to you by IBM. >>We're back in Miami and you're watching the cubes coverage of the IBM data and a I forum. Tony hi. Amiss here is a distinguished engineer for Ditch the Digital and Cloud Business Analytics at IBM. Tony, first of all, congratulations on being a distinguished engineer. That doesn't happen often. Thank you for coming on the Cube. Thank you. So your area focus is on the B I and the Enterprise performance management space. >>Um, and >>if I understand it correctly, a big mission of yours is to try to modernize those make himself service, making cloud ready. How's that going? >>It's going really well. I mean, you know, we use things like B. I and enterprise performance management. When you really boil it down, there's that's analysis of data on what do we do with the data this useful that makes a difference in the world, and then this planning and forecasting and budgeting, which everyone has to do whether you are, you know, a single household or whether you're an Amazon or Boeing, which are also some of our clients. So it's interesting that we're going from really enterprise use cases, democratizing it all the way down to single user on the cloud credit card swipe 70 bucks a month >>so that was used to be used to work for Lotus. But Cognos is one of IBM's largest acquisitions in the software space ever. Steve Mills on his team architected complete transformation of IBM is business and really got heavily into it. I think I think it was a $5 billion acquisition. Don't hold me to that, but massive one of the time and it's really paid dividends now when all this sort of 2000 ten's came in and said, Oh, how Duke's gonna kill all the traditional b I traditional btw that didn't happen, that these traditional platforms were a fundamental component of people's data strategies, so that created the imperative to modernize and made sure that there could be things like self service and cloud ready, didn't it? >>Yeah, that's absolutely true. I mean, the work clothes that we run a really sticky were close right when you're doing your reporting, your consolidation or you're planning of your yearly cycle, your budget cycle on these technologies, you don't rip them out so easily. So yes, of course, there's competitive disruption in the space. And of course, cloud creates on opportunity for work loads to be wrong, Cheaper without your own I t people. And, of course, the era of digital software. I find it myself. I tried myself by it without ever talking to a sales person creates a democratization process for these really powerful tools that's never been invented before in that space. >>Now, when I started in the business a long, long time ago, it was called GSS decision support systems, and they at the time they promised a 360 degree view with business That never really happened. You saw a whole new raft of players come in, and then the whole B I and Enterprise Data Warehouse was gonna deliver on that promise. That kind of didn't happen, either. Sarbanes Oxley brought a big wave of of imperative around these systems because compliance became huge. So that was a real tailwind for it. Then her duke was gonna solve all these problems that really didn't happen. And now you've got a I, and it feels like the combination of those systems of record those data warehouse systems, the traditional business intelligence systems and all this new emerging tech together are actually going to be a game changer. I wonder if you could comment on >>well so they can be a game changer, but you're touching on a couple of subjects here that are connected. Right? Number one is obviously the mass of data, right? Cause data has accelerated at a phenomenal pace on then you're talking about how do I then visualize or use that data in a useful manner? And that really drives the use case for a I right? Because A I in and of itself, for augmented intelligence as we as we talk about, is only useful almost when it's invisible to the user cause the user needs to feel like it's doing something for them that super intuitive, a bit like the sort of transition between the electric car on the normal car. That only really happens when the electric car can do what the normal car can do. So with things like Imagine, you bring a you know, how do cluster into a B. I solution and you're looking at that data Well. If I can correlate, for example, time profit cost. Then I can create KP eyes automatically. I can create visualizations. I know which ones you like to see from that. Or I could give you related ones that I can even automatically create dashboards. I've got the intelligence about the data and the knowledge to know what? How you might what? Visualize adversity. You have to manually construct everything >>and a I is also going to when you when you spring. These disparage data sets together, isn't a I also going to give you an indication of the confidence level in those various data set. So, for example, you know, you're you're B I data set might be part of the General ledger. You know of the income statement and and be corporate fact very high confidence level. More sometimes you mention to do some of the unstructured data. Maybe not as high a confidence level. How our customers dealing with that and applying that first of all, is that a sort of accurate premise? And how is that manifesting itself in terms of business? Oh, >>yeah. So it is an accurate premise because in the world in the world of data. There's the known knowns on the unknown knowns, right? No, no's are what you know about your data. What's interesting about really good B I solutions and planning solutions, especially when they're brought together, right, Because planning and analysis naturally go hand in hand from, you know, one user 70 bucks a month to the Enterprise client. So it's things like, What are your key drivers? So this is gonna be the drivers that you know what drives your profit. But when you've got massive amounts of data and you got a I around that, especially if it's a I that's gone ontology around your particular industry, it can start telling you about drivers that you don't know about. And that's really the next step is tell me what are the drivers around things that I don't know. So when I'm exploring the data, I'd like to see a key driver that I never even knew existed. >>So when I talk to customers, I'm doing this for a while. One of the concerns they had a criticisms they had of the traditional systems was just the process is too hard. I got to go toe like a few guys I could go to I gotta line up, you know, submit a request. By the time I get it back, I'm on to something else. I want self serve beyond just reporting. Um, how is a I and IBM changing that dynamic? Can you put thes tools in the hands of users? >>Right. So this is about democratizing the cleverness, right? So if you're a big, broad organization, you can afford to hire a bunch of people to do that stuff. But if you're a startup or an SNB, and that's where the big market opportunity is for us, you know, abilities like and this it would be we're building this into the software already today is I'll bring a spreadsheet. Long spreadsheets. By definition, they're not rows and columns, right? Anyone could take a Roan Collin spreadsheet and turn into a set of data because it looks like a database. But when you've got different tabs on different sets of data that may or may not be obviously relatable to each other, that ai ai ability to be on introspect a spreadsheet and turn into from a planning point of view, cubes, dimensions and rules which turn your spreadsheet now to a three dimensional in memory cube or a planning application. You know, the our ability to go way, way further than you could ever do with that planning process over thousands of people is all possible now because we don't have taken all the hard work, all the lifting workout, >>so that three dimensional in memory Cuba like the sound of that. So there's a performance implication. Absolutely. On end is what else? Accessibility Maw wraps more users. Is that >>well, it's the ability to be out of process water. What if things on huge amounts of data? Imagine you're bowing, right? Howdy, pastors. Boeing How? I don't know. Three trillion. I'm just guessing, right? If you've got three trillion and you need to figure out based on the lady's hurricane report how many parts you need to go ship toe? Where that hurricane reports report is you need to do a water scenario on massive amounts of data in a second or two. So you know that capability requires an old lap solution. However, the rest of the planet other than old people bless him who are very special. People don't know what a laugh is from a pop tart, so democratizing it right to the person who says, I've got a set of data on as I still need to do what if analysis on things and probably at large data cause even if you're a small company with massive amounts of data coming through, people click. String me through your website just for example. You know what if I What if analysis on putting a 5% discount on this product based on previous sales have that going to affect me from a future sales again? I think it's the democratizing as the well is the ability to hit scale. >>You talk about Cloud and analytics, how they've they've come together, what specifically IBM has done to modernize that platform. And I'm interested in what customers are saying. What's the adoption like? >>So So I manage the Global Cloud team. We have night on 1000 clients that are using cloud the cloud implementations of our software growing actually so actually Maur on two and 1/2 1000. If you include the multi tenant version, there's two steps in this process, right when you've got an enterprise software solution, your clients have a certain expectation that your software runs on cloud just the way as it does on premise, which means in practical terms, you have to build a single tenant will manage cloud instance. And that's just the first step, right? Because getting clients to see the value of running the workload on cloud where they don't need people to install it, configure it, update it, troubleshoot it on all that other sort of I t. Stuff that subtracts you from doing running your business value. We duel that for you. But the future really is in multi tenant on how we can get vast, vast scale and also greatly lower costs. But the adoptions been great. Clients love >>it. Can you share any kind of indication? Or is that all confidential or what kind of metrics do you look at it? >>So obviously we look, we look a growth. We look a user adoption, and we look at how busy the service. I mean, let me give you the best way I can give you is a is a number of servers, volume numbers, right. So we have 8000 virtual machines running on soft layer or IBM cloud for our clients business Analytics is actually the largest client for IBM Cloud running those workloads for our clients. So it's, you know, that the adoption has been really super hard on the growth continues. Interestingly enough, I'll give you another factoid. So we just launched last October. Cognos Alex. Multi tenant. So it is truly multi infrastructure. You try, you buy, you give you credit card and away you go. And you would think, because we don't have software sellers out there selling it per se that it might not adopt as much as people are out there selling software. Okay, well, in one year, it's growing 10% month on month cigarette Ally's 10% month on month, and we're nearly 1400 users now without huge amounts of effort on our part. So clearly this market interest in running those softwares and then they're not want Tuesdays easer. Six people pretending some of people have 150 people pretending on a multi tenant software. So I believe that the future is dedicated is the first step to grow confidence that my own premise investments will lift and shift the cloud, but multi tenant will take us a lot >>for him. So that's a proof point of existing customer saying okay, I want to modernize. I'm buying in. Take 1/2 step of the man dedicated. And then obviously multi tenant for scale. And just way more cost efficient. Yes, very much. All right. Um, last question. Show us a little leg. What? What can you tell us about the road map? What gets you excited about the future? >>So I think the future historically, Planning Analytics and Carlos analytics have been separate products, right? And when they came together under the B I logo in about about a year ago, we've been spending a lot of our time bringing them together because, you know, you can fight in the B I space and you can fight in the planning space. And there's a lot of competitors here, not so many here. But when you bring the two things together, the connected value chain is where we really gonna win. But it's not only just doing is the connected value chain it and it could be being being vice because I'm the the former Lotus guy who believes in democratization of technology. Right? But the market showing us when we create a piece of software that starts at 15 bucks for a single user. For the same power mind you write little less less of the capabilities and 70 bucks for a single user. For all of it, people buy it. So I'm in. >>Tony, thanks so much for coming on. The kid was great to have you. Brilliant. Thank you. Keep it right there, everybody. We'll be back with our next guest. You watching the Cube live from the IBM data and a I form in Miami. We'll be right back.

>>from our studios in the heart of Silicon Valley. Palo ALTO, California It is a cute conversation. >>Hi, and welcome to the Cube studios for another cube conversation where we go in depth with thought leaders driving innovation across the technology industry. I'm your host, Peter Boris. Digital businesses affecting every enterprise of every size, small and large, and the types of solutions that required the types of outcomes that are being pursued are extremely complex and require an enormous amount of work from some of the best and brightest people on the business side as well as the technology side. And that means not just from a large company. It means from an entire ecosystem of potential sources of genius and insight and good hard work. So the consequence for every enterprises, how do they cobble together that collection of experts and capabilities that are gonna help them transform their business more successfully, Maur completely and more certainly than they would otherwise? And that's we're gonna talk about today. Today we're here with Kelly Ireland, who's the founder and C E o. C. B Technologies. Kelly. Welcome to the >>Cube. Thank you, Peter. Happy to be here, >>so let's start by finding a little bit about CV Technologies to also about what you do. >>Um, I have a IittIe background, so I have been in it for 40 years. In 2001 I decided I had a better idea of how to both support clients as well as my employees. So I opened CB Technologies were value added reseller, um, and then say about five years ago, I decided to do some transforming of the company itself. I saw what was going on in the industry, and I thought this was the time for us to get going. Turned out we were a little early, but we wanted to transform from what you would call it the value added reseller two systems integrator. Because that was the only words what they had for. You know what that end result would be? Now I've heard it's the, um, domain expert integrator, which we like a lot better. And what we've done is gone from this value add, which we've all seen over the last couple of decades, into actually engineering solutions, and mostly with consortiums, which will talk about of the O. T. I t. Convergence and what's going to be needed for that to make our customers successful. >>Well, you just described. In many respects, the vision that businesses have had and how it's changed over years were first. The asset was the hardware. Hence the var. Today, the asset really is the date of the application and how you're going to apply that to change the way your business operates the customer experiences, you provide the profitability that you're able to return back to shareholders. So let's dig into this because that notion of data that notion of digital transformation is especially important in a number of different names, perhaps no more important than in the whole industrial and end of things domain. That intersection of I t know Tia's, you said, Tell us a little bit about what you're experiencing with your customers as they try to think about new ways of applying technology technology rich data to their business challenges. >>We'll use the perfect word you said dig, because this is all about layers. It's all about it was technology and software. Now it's about technology, software and integration. In fact, the conversations were having with our clients. Right now we don't even talk about a no Yim's name. Where before you would. But we haven't our head. What? We know what would be best. What we look at now is the first thing you do is go in and sit down with the client. And not only with the client, the you know, the executives or the C I or the C T. O's et cetera, but the employees themselves. Because what we've seen with I I I o t o t i t Convergence, it's You have to take into account what the worker needs and the people that are addressing it that way. Um, this project that we started with Hewlett Packard Enterprise, they started up what we call the refinery of the future. It could be acts of the future. It doesn't really matter. But it was getting at least up to five use cases with a consortium of partner companies that could go address five different things within the refinery. And the reason that I think it's been so successful is that the owner, the CEO Doug Smith and the VP of ops Linda Salinas, immediately wrap their arms around bringing employees. They're a small company there, maybe 50. They brought half of them to HPD Lab to show them what a smart pump laws for their chemical plant text. More chemical in Galina Park in Texas. Starting from that, it was like they put him on a party bus, took them down, put them in the lab, told them, showed them what a smart pump was and all of a sudden the lights turned on for the workers. These are people that have been, you know, manual valves and turning knobs and, you know, looking at computer screens they'd never seen what a smart, censored pump waas all of it sudden on the drive back to the company, ideas started turning. And then HP took it from there, brought in partners, sat everybody in the room, and we started feathering out. Okay, what's needed. But let's start with what the client needs. What do those different business users within the chemical plant need, and then build use cases from that? So we ended up building five use cases. >>Well, so what? Get another five years cases in a second? But you just described something very interesting, and I think it's something that partners have historically been able to do somewhat uniquely on that is that the customer journey is not taken by just an individual within the business. What really happens is someone has an idea. They find someone, often a partner, that can help them develop that idea. And then they go off and they recruit others within their business and a local partner that has good domain expertise at the time. And energy and customer commitment could be an absolutely essential feature of building the consensus within the organization to really accelerate that customer journey. If I got that right? >>Absolutely, absolutely. And what we saw with Refinery of the Future was getting those partnerships HP East started. It created the project kind of through information out to many of their ecosystem partners trying to gain interest because the thing was is this was kind of our bet was a very educated bet, but it's our bet to say, Yeah, we think this makes sense. So, you know, like I said, I think there's about 14 partners that all joined in both on the I t om side the ot oh am side and then both Deloitte and CB Technologies for the S. I and like expert domain expert integration where you really get into How do you tie OT and I t together? >>All right, so we've got this situation where this is not As you said, It's not just in the refining process, manufacturing businesses. It's in a lot of business. But in this particular one, you guys have actually fashioned what you call the refinery of of the future has got five clear use cases. Just give us an example of what those look like and how you've been RCB technology has been participated in the process of putting those together. >>Um, the 1st 1 was pretty wrapped around Predictive Analytics, and that was led by Deloitte and has a whole host of OT and I t integration on it >>again, not limited to process manufacturing at all >>at all, but and a good group, you know, you have national instruments, Intel flow. Serve. Oh, it's ice off Snyder Electric, PTC riel, where they're such a host >>of the >>consortium and I I think what was most important to start this whole thing was H P E. Came in and said, Here's an MOU. Here's a contract. You all will be contract ID to the overall resorts results. Not just your use case. Not just one or two use cases you're in, but all five because they all can integrate in some sense so >>that all can help. Each of you can help the others think. Problems. Truce. That's the 1st 1 about the 2nd 1 >>The 2nd 1 is video is a sensor that was Intel CB Technologies. I think we have as you're in there as well, doing some of the analytics, some P T. C. And what that was all about was taking video. And, you know, taking a use case from Linda and saying, Where where do you need some sort of video analytics Taking that processing it and what we ended up doing with that one was being able to identify, you know, animals or aggressive animals within the train yard. A downed worker transients that shouldn't be there because we can't decipher between you know, someone that's in text marks p p ease versus somebody that's in street clothes. So taking all that analyzing the information, the pictures, training it to understand when it needs to throw and alert >>lot of data required for that. And that's one of the major major drivers of some of the new storage technologies out there. New fabrics that are out there. How did that play? A role? >>As you can imagine, H p E is the under underlying infrastructure across the entire refinery. The future from compute with the, uh, EJ data center into the Reuben network into nimble storage for storing on site. Um, what we're finding, no matter who we talked to in the industry, it is. Most of them still want to keep it on Prem. In some sense, security. They're still all extremely cautious. So they want to keep it on Prem. So having the nimble storage right in the date, having the edge data center having everything in the middle of this chemical plant was absolutely a necessity. And having all of that set up having my team, which was the C B Tech team that actually did all the integration of setting up the wireless network, because guess what? When you're in a different kind of environment, not inside a building, you're out where there's metal pumps. There's restrictions because ah, flash could cause an explosion so intrinsically safe we had to set up all that and determined how? How could we get the best coverage? Especially? We want that video signal to move quite fast over the WiFi. How do we get all that set up? So it takes the most advantage of, you know, the facility and the capabilities of the Aruban network. >>So that's 12345 quickly were >>three worker safety, which hasn't started yet. We're still waiting for one of the manufacturers to get the certification they need. Um, four we have is connected worker, which is on fire, having a work >>of connected worker on fire and worker >>safety. >>Yeah, they don't sound, but just think of all the data and having the worker have it right at his fingertips. And, oh, by the way, hands free. So they're being ableto to take in all this data and transmit data, whether it's by voice or on screen back >>from a worker central perspective, from one that sustains the context of where the worker is, what stress there under what else? They've got to do it said. >>And and what are they trying to complete and how quickly? And that's where right now we have r A y that's in the 90% which is off the chart. But it's and and what's great about being at Text Mark is we actually can prove this. I can have somebody walk with me, a client that wants to look at it. They can go walk the process with me, and they will immediately see that we reduce the time by 90%. >>So I've given your four. What's the 5th 1? >>Acid intelligence, which is all about three D Point Cloud three D visualization. Actually being able to pull up a smart pump. You know it really? Any pump, you scan the facility you converted into three D and then in the program that we're using, you can actually pull up a pump. You can rotate it 360 degrees. It's got a database behind it that has every single bit of asset information connected videos, cad cams, P and I. D s. For the oil and gas industry. Everything's in their e mails could be attached to it, and then you can also put compliance reports. So there you might need to look a corrosion. One of those tests that they do on a you know, annual or every five year basis. That's point and click. You pull it up and it tells you where it sits, and then it also shows you green, yellow, red. Anything in red is immediate, attest that tension yellow is you need to address it greens. Everything's 100% running. >>So the complexity that we're talking about, the kind of specificity of these solutions, even though they can be generalized. And you know, you talked about analytics all the way out to asset optimization Intel intelligence. There are We can generalize and structure, but there's always going to be, it seems to us there's going to be a degree of specificity that's required, and that means we're not gonna talk about package software that does this kind of stuff. We're talking about sitting down with a customer with a team of experts from a lot of different places and working together and applying that to achieve customer outcome. So I got that right >>absolutely, and what we did with the consortium looking at everything. How they first addressed it was right along that line, and if you look at software development, agile following agile process, it's exactly what we're doing in four I I o T o R O T I t Convergence, because if you don't include all of those people, it's never going to be successful. I heard it a conference the other day that said, POC is goto I ot to die, and it's because a lot of people aren't addressing it the right way. We do something called Innovation Delivery as a service, which is basically a four day, 3 to 4 day boot camp. You get all the right people in, in in the room. You pull in everything from them. You boot out the executive team partway through, and you really get in depth with workers and you have them say what they wouldn't say in front of their bosses that this happened with Doug and Linda and Linda said it was mind blowing. She goes. I didn't realize we had so many problems because she came back in the room and there was a 1,000,000 stickies. And then she said, the more she read it and the more you know, we refined it down, she said it was absolutely delivered, you know, the use case that she would have eventually ended up with, but loved having all the insights from, >>well, work. Too often, tech companies failed to recognize that there's a difference between inventing something and innovation. Inventing is that engineering act of taking what you know about physics or social circumstance Secreting hardware software innovation is a set of social acts that get the customer to adopt it, get a marketplace to adopt it, change their behaviors. And partners historically have been absolutely essential to driving that innovation, to getting customers to actually change the way to do things and embed solutions in their operations. And increasingly, because of that deep knowledge with customers are trying to doing, they're participating. Maurine, the actual invention process, especially on the softer side of you said, >>Yeah, yeah, I think what's really interesting in this, especially with Coyote. When I look back a few years, I look at cloud and you know everything was cloud and everybody ran to it and everybody jumped in with both feet, and then they got burned. And what we're seeing with this whole thing with I o t you would think we're showing these are lies, return on. Investments were showing all this greatness that can come out of it and and they're very slow at sticking their toe in. But what we've found is no one arrives should say the majority of corporations anymore don't want to jump in and say, Let's do it two or five or $10 million project. We see your power point. No, let's let's depart Owen with with what we're doing, it's, you know, a really small amount of money to go in and really direct our attention at exactly what their problem is. It's not off the shelf. It's but it's off the shelf with customization. It's like we've already delivered on connected worker for oil and gas. But now we're are so starting to deliver multiple other industries because they actually walk through text mark. We could do tours, that text mark. That was kind of the trade off. All these partners brought technology and, you know, brought their intelligence and spent. We were now on two years of proving all this out. Well, they said, Fine, open the kimono will let your customers walk through and see it >>makes text mark look like a better suppliers. >>Well, it's enhanced their business greatly. I can tell you they're just starting a new process in another week. And it was all based on people going through, you know, a client that went through and went. Wait >>a minute. I >>really like this. There are also being able to recruit technologists within the use in industry, which you would think text marks 50 employees. It's a small little plant. It's very specialized. It's very small. They pulled one of the top. Uh, sorry. Lost not. I'm trying to think of what the name >>they're. They're a small number of employees, but the process manufacturing typically has huge assets. And any way you look at it, we're talking about major investments, major monies that require deep expertise. And my guess is the text Mark is able to use that to bring an even smarter and better >>people smarter and better. People that are looking at it going they're ahead of the curve, for they're so far ahead of the curve that they want to be on board were that they're bringing in millennials on they're connected. Worker Carlos is there trainload lead. And he dropped an intrinsically safe camera and it broke and he tried to glue it together, tried to super glue it together. And then he ran back to Linda and he said I broke the case and this case is like £10. They call it the Brick. They gotta lug it up. They got to climb up the train car, leg it up, take a picture that they have sealed the valves on all the cars before they leave. Well, he had used the real where had, you know, device. And he went into Linda and he said, I know there's a camera in there. There's camera capabilities. Can I use that until we get another case? And she's like, Yeah, go ahead. Well, he went through, started using that toe like lean over, say, Take photo. We engineered that it could go directly back to the audit file so that everybody knew the minute that picture was taken, it went back into the audio file. This is where we found the process was reduced by 90% of time. But he turned around and trained his entire team. He wasn't asked to, but he thought, this is the greatest thing. He went in trainable. And now, about every two weeks, Carlos walks in to my team that sits a text mark and comes up with another use case for connected worker. It's amazing. It's amazing what you know were developed right out of the customer by using their workers and then, you know, proactively coming to us going. Hey, I got another idea. Let's add this where I think at version 7.0, for connected worker. Because of that feedback because of that live feed back in production. >>Great story, Kelly. So, once again, Callie Ireland is a co founder and CEO of CB Technologies. Thanks for being on the tube. >>Thank you for having me >>on once again. I wanna thank all of you for joining us for another cute conversation. I'm Peter burgers. See you next time.

>> Narrator: Live from Atlanta, Georgia it's theCube covering AnsibleFest 2019, brought to you by Red Hat. >> Welcome back, this is theCube's coverage of AnsibleFest 2019, here in Atlanta Georgia, I'm Stu Miniman, real excited to be at this event for the first time, getting to talk to a number of the practitioners, talking to some of the executives, and to give us a slightly different angle on it, we're really going to talk about education and what's happening in this space. And joining me, first time guest on the program, Carlos Caicedo, who is an associate professor at Syracuse University. Carlos thanks so much for joining us. >> Thank you, thanks for inviting me. >> All right, so Syracuse, the snow belt hasn't hit yet for 2019 up your neck of the woods, - [Carlos] Yeah. >> but you know tell us a bit about what you know, you do, the programs you work on, and then we'll get into how much automation is a piece of that. >> Okay, so I'm professor at the School of Information Studies at Syracuse University and two years ago, we decided to launch a new masters degree program on enterprise data systems that focuses on cloud technologies, automation, scripting and all that's required now a days to manage and work with the infrastructure that data centric enterprises need now a days. Basically we saw this need because the traditional way of working with infrastructure, from the command line interface wasn't going to cut it anymore. You need to work with scale, new concepts, APIs, git, continualization, virtualization. So we needed to create a program that replace our traditional networking program and modernize it and bring it up to speed with what's currently happening in the industry. >> I think that's great, you know we talk about what is, how do we close the gap between what, you know, business needs, what skillsets are needed, and what's coming out of university. You know for a long time, it was like okay, let's get everybody in computer sciences and do that, but you know, whatever programming language you learn today, it's like oh boy, it seems to change and be out of date there, and if you talk about a masters degree, in IT we're working with, you know, how does the technology and the business, how do they work together. - [Carlos] Yeah. So I have to imagine that this, that masters level helps prepare your students to kind of live in that world. >> Yes, we're a bit different than what you would call a traditional network engineering degree, which focuses a lot on the technology. We imbed or try to give our students also a business perspective so they learn management, information management, or management concepts for information professionals, information policy concepts, so you understand the business side, but then we also imbed a lot of technology components into the curriculum. So the idea is to have this kind of multi-disciplinary hybrid professional, that understands that whatever is being worked at the infrastructural level needs to support the goals of the business and can walk those worlds, be a good participant in teams. Collaboration is the key now a days as we've seen. >> So Carlos, what prerequisites do your students have to have coming in, I mean do they need to be certified on certain network gear or you know what do they need to understand, and what do you give them that might be different than what they would have gotten out in industry? >> Well, preferably, students that come in should have some knowledge of networking the TCP IP stack, basically, what routing an IP address is, and from there on they'll see courses on advanced networking, scripting, cloud management, cloud architecture, and so forth, and plus the business side as I mentioned, to get them prepared for the real world. >> Okay, one of the things that was, you know, greatly talked about here is really that evolution of automation. You know how do we move it from being a just you know, tactical. One of the keynotes speakers yesterday talked about the whack-a-mole I'm going to solve all of these little problems to a more strategic view. How have you been seeing in, how does the evolution of automation impact your curriculum? >> Well, that's a great question. So the idea is not to have automation for the sake of automation. Like you said we need the business focus and whoever is participating in a team and moving the automation story forward needs to be conscious that the end goal is to support business. However, in terms of how it has impacted our curriculum, we embedded automation in several of our courses because that's the way to go in the future, you can't just cut it with, you know, a device by device kind of approach. So everything now a days changes too quickly and the demands for businesses to respond to these changes require a quick turnaround for whatever the infrastructure needs to provide to support the business. So we need to build professionals that understand this and can apply innovation to their benefit and to the benefit of their enterprise. >> Now one of the interesting conversations we've had this week is that the software, the technology, is actually hoping to drive some of the collaboration and communication between groups and roles. How much of that, does that get touched on at all, you know when you talk about working with the business and doing all that? >> Yes, so we kind of build on team based assignments and labs just to get students to understand they're going to have to be part of a team. And you might have people that speak a different language than you or at a different level than you. Let's say more business-oriented, more process-oriented, more technology-oriented but you have to be, well at least a professional would prefer, you have to be that glue that keeps the team cohesive and working together to a common goal. So yeah, collaboration is key and we've seen that in this event, it's all about changing the culture and having this positive approach towards being collaborative. And we're hoping that we're building professionals that from day one understand this and can be part of a team. >> All right, so you talk about that collaboration, I'm curious, in higher education, you know, how is what you're doing impacting your peers, how do you learn from both your peers and education as well as in industry? >> Well, so, at least at our university we have a culture of collaboration between different departments and disciplines. We might work a lot with engineering, we might work a lot with the business school, law school. So again, to bring this interdisciplinary knowledge to students. We also like to reach out to industry and build partnerships, build bridges so that we can leverage some of the resources they have you know to promote or educate people on their products, but also to get students to actually be very hands on and work with things that are out there in the real world. So the idea is that they can speak the same language as many professionals that are already out there. >> Can you speak to you know, Red Hat's participation, how are they partnering and enabling what your mission is? >> So I've been using Ansible in several of my courses and so we have a scripting course, just to mention one, where we do a lot of modules on Ansible and again to understand this concept of mass automation, that automation is the key element for moving infrastructure and having infrastructure deliver goals in the future. So we partner in such that we get to use their products in an easy way and we keep on building new bridges to use more of their products. Now with the announcement of the automation platform, I really want to dig into that and start building new labs for students on that platform. >> Stu: Okay so sounds like you're excited by the announcement. - Oh yeah. >> Anything particular that you know caught your eye on that? It sounds like, you know, the networking pieces with collections seems like something that might be useful. >> Yes, so, well being an information school we're big on data right, so now you have the story of being able to automate a device or a service level, putting that into Ansible tower, doing access control, monitoring and then collecting statistics based on that. Monitoring the performance of your playbooks, monitoring the performance of your automation tasks. So having that data, that analytic side for example, is quite exciting for an information school because we might get some ideas as to how to leverage that in the future. >> So I'm wondering if you could share kind of, you know, what your students think about automation in general. You know if you talk about just the general workforce, you know, over the years there's sometimes that fear oh the robots and the automations are going to takeover you know, what I'm doing, you know, is there any of that fear from the generation, or does working with the technology, you know, help enable what they're looking to do? >> Well, it's definitely kind of a mixed bag. So until students get introduced to tools like Ansible, they do have some fear that well now it's like one person can do the work of 20 or 30 people. But once they understand the story of, you know, tools like Ansible, they change their focus. I had two students at the AnsibleFest last year and they were amazed about looking at the way that many enterprises are using automation. So it's not just about taking out these mundane tasks that network managers have to do, it's getting the time to actually innovate, to be creative, get rid of those tasks that occupy time but are not really important, minimum tasks to get the ship moving along, but then build on top of that to create new products and services. >> It's interesting if you look at the research on it, you know, information technology often has not had the efficiency increases of kind of worker productivity that you might expect and definitely not to the point that it's going to be, you know, massive, you know, job, you know, killing of jobs, you know, hopefully, you know, when we talk to some of the people here it should improve your job satisfaction, hopefully get rid of some of those oh my gosh I got to spend, you know, every fifth weekend, you know, working on this and we can automate some of those away, but yeah there's that disconnect between the reality and, you know, what the technology's actually doing. >> Yes, yeah you don't want to be putting fires every weekend or everyday. And you want to bring additional, how you do to the enterprise and I think that's what automation allows in a big way. >> Great so Carlos you've been to AnsibleFest before, give us your impression so far, the event this week and some of the key things that you, you know, have been or are looking to take away from AnsibleFest 2019. >> Well as I mentioned before, the automation platform definitely want to look into that. I think the way that people are talking about collaboration around automation is very important. I think that kind of validates the team based focus and approach to some of our assignments at least at the program level. Also, I think that the way that companies are now telling their stories of automation. It's pretty neat, I hope to bring some of them into the curriculum. I just saw one from these guys from New Zealand, that they had come, they had videos as to how they implemented some big massive automation and tasks. That was pretty interesting. So hopefully I get to take some of what I've learned here into the curriculum. >> And you know, just a final thing, you know, how prevalent are these, you know, curriculums of automation throughout the country, you know, any data on that? >> Well that's a good question, so basically I would split the university so the program's like in three groups. So you have one group that's developing programs mostly on the network engineering side, very very technical. Other group that probably hasn't really catched on the evolution of networking and probably just teaching networking in the same traditional manner, you know, hoping to get people prepared for cisco certification, certifications of other types, very static, traditional network construction. And then another group which would be kind of in the middle where it's not fully about the technology, it's also about the business and how much you concentrate on both sides can, is where we can distinguish each of these programs. So, besides us I think there are a couple smaller universities that are also preparing these transitions. It's a hard thing to do because things change so quickly and it's hard for faculty to keep up and we want to deliver up to date content to students and it's extremely difficult. My content changes by at least a third from year to year, so I have prepare new slides, new assignments, new labs, get more infrastructure. It's very exciting, but also very challenging and so, we hope that our students are built to embrace change, prepare for it and not oppose it. >> I think it's a great mission, you know, but not only does you know, the technology and the business need to work close together but we know that the only constant in our industry is change. - [Carlos] Yes. So being prepared for, as a workforce, to be able to, you know, live in that and thrive in that environment is so critically important. Carlos, thank you so much for sharing with us, you know, the curriculum at Syracuse and, you know, we look forward to catching up with you in the future. >> Thank you. >> All right we'll be back with lots more coverage, I'm Stu Miniman, John Furrier is also in the house, it's our two days live coverage here from AnsibleFest 2019. Atlanta, thanks for watching theCube.

>> live from San Francisco, California. It's the Q covering the IBM Chief Data Officer Summit brought to you by IBM. >> Welcome back to Fisherman's Fisherman's Wharf in San Francisco. Everybody, my name is David wanted. You're watching the Cube, the leader in live tech coverage, you ought to events. We extract the signal from the noise. We're here. The IBM CDO event. This is the 10th anniversary of this event. Caitlin Hallford is here. She's the director of a I Accelerator and client success at IBM. Caitlin, great to see you again. Wow. 10 years. Amazing. They and Carlo Apple Apple Glace e is here. Who is the program director for data and a I at IBM. Because you again, my friend. Thanks for coming on to Cuba. Lums. Wow, this is 10 years, and I think the Cube is covered. Probably eight of these now. Yeah, kind of. We bounce between San Francisco and Boston to great places for CEOs. Good places to have intimate events, but and you're taking it global. I understand. Congratulations. Congratulations on the promotion. Thank you. Going. Thank you so much. >> So we, as you know well are well, no. We started our chief date officer summits in San Francisco here, and it's gone 2014. So this is our 10th 1 We do two a year. We found we really have a unique cohort of clients. The join us about 100 40 in San Francisco on the spring 140 in Boston in the fall, and we're here celebrating the 10th 10 Summit. >> So, Carlo, talk about your role and then let's get into how you guys, you know, work together. How you hand the baton way we'll get to the client piece. >> So I lead the Data Center League team, which is a group within our product development, working side by side with clients really to understand their needs as well developed, use cases on our platform and tools and make sure we are able to deliver on those. And then we work closely with the CDO team, the global CEO team on best practices, what patterns they're seeing from an architecture perspective. Make sure that our platforms really incorporating that stuff. >> And if I recall the data science that lead team is its presales correct and could >> be posted that it could, it really depends on the client, so it could be prior to them buying software or after they bought the software. If they need the help, we can also come in. >> Okay, so? So it can be a for pay service. Is that correct or Yeah, we can >> before pay. Or sometimes we do it based on just our relation with >> It's kind of a mixed then. Right? Okay, so you're learning the client's learning, so they're obviously good, good customers. And so you want to treat him right >> now? How do you guys work >> together? Maybe Caitlin, you can explain. The two organizations >> were often the early testers, early adopters of some of the capabilities. And so what we'll do is we'll test will literally will prove it out of skill internally using IBM itself as an example. And then, as we build out the capability, work with Carlo and his team to really drive that in a product and drive that into market, and we share a lot of client relationships where CEOs come to us, they're want advice and counsel on best practices across the organization. And they're looking for latest applications to deploy deploy known environments and so we can capture a lot of that feedback in some of the market user testing proved that out. Using IBM is an example and then work with you to really commercialized and bring it to market in the most efficient manner. >> You were talking this morning. You had a picture up of the first CDO event. No Internet, no wife in the basement. I love it. So how is this evolved from a theme standpoint? What do you What are the patterns? Sure. So when >> we started this, it was really a response. Thio primarily financial service is sector regulatory requirements, trying to get data right to meet those regulatory compliance initiatives. Defensive posture certainly weren't driving transformation within their enterprises. And what I've seen is a couple of those core elements are still key for us or data governance and data management. And some of those security access controls are always going to be important. But we're finding his videos more and more, have expanded scope of responsibilities with the enterprise they're looked at as a leader. They're no longer sitting within a c i o function there either appear or, you know, working in partnership with, and they're driving enterprise wide, you know, initiatives for the for their enterprises and organizations, which has been great to see. >> So we all remember when you know how very and declared data science was gonna be the number one job, and it actually kind of has become. I think I saw somewhere, maybe in Glass door was anointed that the top job, which is >> kind of cool to see. So what are you seeing >> with customers, Carlo? You guys, you have these these blueprints, you're now applying them, accelerating different industries. You mentioned health care this morning. >> What are some >> of those industry accelerators And how is that actually coming to fruition? Yes. >> So some of the things we're seeing is speaking of financial clients way go into a lot of them. We do these one on one engagements, we build them from custom. We co create these engineering solutions, our platform, and we're seeing patterns, patterns around different use cases that are coming up over and over again. And the one thing about data science Aye, aye. It's difficult to develop a solution because everybody's date is different. Everybody's business is different. So what we're trying to do is build these. We can't just build a widget that's going to solve the problem, because then you have to force your data into that, and we're seeing that that doesn't really work. So building a platform for these clients. But these accelerators, which are a set of core code source code notebooks, industry models in terms a CZ wells dashboards that allow them to quickly build out these use cases around a turn or segmentation on dhe. You know some other models we can grab the box provide the models, provide the know how with the source code, as well as a way for them to train them, deploy them and operationalize them in an organization. That's kind of what we're doing. >> You prime the pump >> prime minute pump, we call them there right now, we're doing client in eights for wealth management, and we're doing that, ref SS. And they come right on the box of our cloudpack for data platform. You could quickly click and install button, and in there you'll get the sample data files. You get no books. You get industry terms, your governance capability, as well as deployed dashboards and models. >> So talk more about >> cloudpack for data. What's inside of that brought back the >> data is a collection of micro Service's Andi. It includes a lot of things that we bring to market to help customers with their journey things from like data ingestion collection to all the way Thio, eh? I model development from building your models to deploying them to actually infusing them in your business process with bias detection or integration way have a lot of capability. Part >> of it's actually tooling. It's not just sort of so how to Pdf >> dualism entire platform eso. So the platform itself has everything you need an organization to kind of go from an idea to data ingestion and governance and management all the way to model training, development, deployment into integration into your business process. >> Now Caitlin, in the early days of the CDO, saw CDO emerging in healthcare, financialservices and government. And now it's kind of gone mainstream to the point where we had Mark Clare on who's the head of data neighborhood AstraZeneca. And he said, I'm not taking the CDO title, you know, because I'm all about data enablement and CDO. You know, title has sort of evolved. What have you seen? It's got clearly gone mainstream Yep. What are you seeing? In terms of adoption of that, that role and its impact on organizations, >> So couple of transit has been interesting both domestically and internationally as well. So we're seeing a lot of growth outside of the U. S. So we did our first inaugural summit in Tokyo. In Japan, there's a number of day leaders in Japan that are really eager to jump start their transformation initiatives. Also did our first Dubai summit. Middle East and Africa will be in South Africa next month at another studio summit. And what I'm seeing is outside of North America a lot of activity and interest in creating an enabling studio light capability. Data Leader, Like, um, and some of these guys, I think we're gonna leapfrog ahead. I think they're going to just absolutely jump jump ahead and in parallel, those traditional industries, you know, there's a new federal legislation coming down by year end for most federal agencies to appoint a chief data officer. So, you know, Washington, D. C. Is is hopping right now, we're getting a number of agencies requesting advice and counsel on how to set up the office how to be successful I think there's some great opportunity in those traditional industries and also seeing it, you know, outside the U. S. And cross nontraditional, >> you say >> Jump ahead. You mean jump ahead of where maybe some of the U. S. >> Absolute best? Absolutely. And I'm >> seeing a trend where you know, a lot of CEOs they're moving. They're really closer to the line of business, right? They're moving outside of technology, but they have to be technology savvy. They have a team of engineers and data scientists. So there is really an important role in every organization that I'm seeing for every client I go to. It's a little different, but you're right, it's it's definitely up and coming. Role is very important for especially for digital transformation. >> This is so good. I was gonna say one of the ways they are teens really, partner Well, together, I think is weaken source some of these in terms of enabling that you know, acceleration and leap frog. What are those pain points or use cases in traditional data management space? You know, the metadata. So I think you talk with Steven earlier about how we're doing some automated meditate a generation and really using a i t. O instead of manually having to label and tag that we're able to generate about 85% of our labels internally and drive that into existing product. Carlos using. And our clients are saying, Hey, we're spending, you know, hundreds of millions of dollars and we've got teams of massive teams of people manual work. And so we're able to recognize it, adopts something like that, press internally and then work with you guys >> actually think of every detail developer out there that has to go figure out what this date is. If you have a tool which we're trying to cooperate the platform based on the guidance from the CDO Global CEO team, we can automatically create that metadata are likely ingested and provide into platform so that data scientists can start to get value out >> of it quickly. So we heard Martin Schroeder talked about digital trade and public policy, and he said there were three things free flow of data. Unless it doesn't make sense like personal information prevent data localization mandates, yeah, and then protect algorithms and source code, which is an I P protection thing. So I'm interested in how your customers air Reacting to that framework, I presume the protect the algorithms and source code I p. That's near and dear right? They want to make sure that you're not taking models and then giving it to their competitors. >> Absolutely. And we talk about that every time we go in there and we work on projects. What's the I p? You know, how do we manage this? And you know, what we bring to the table with the accelerators is to help them jump start them right, even though that it's kind of our a p we created, but we give it to them and then what they derive from that when they incorporate their data, which is their i p, and create new models, that is then their i. P. So those air complicated questions and every company is a little different on what they're worried about with that, so but many banks, we give them all the I P to make sure that they're comfortable and especially in financial service is but some other spaces. It's very competitive. And then I was worried about it because it's, ah, known space. A lot of the algorithm for youse are all open source. They're known algorithms, so there's not a lot of problem there. >> It's how you apply them. That's >> exactly right how you apply them in that boundary of what >> is P, What's not. It's kind of >> fuzzy, >> and we encourage our clients a lot of times to drive that for >> the >> organisation, for us, internally, GDP, our readiness, it was occurring to the business unit level functional area. So it was, you know, we weren't where we needed to be in terms of achieving compliance. And we have the CEO office took ownership of that across the business and got it where we needed to be. And so we often encourage our clients to take ownership of something like that and use it as an opportunity to differentiate. >> And I talked about the whole time of clients. Their data is impor onto them. Them training models with that data for some new making new decisions is their unique value. Prop In there, I'd be so so we encourage them to make sure they're aware that don't just tore their data in any can, um, service out there model because they could be giving away their intellectual property, and it's important. Didn't understand that. >> So that's a complicated one. Write the piece and the other two seem to be even tougher. And some regards, like the free flow of data. I could see a lot of governments not wanting the free flow of data, but and the client is in the middle. OK, d'oh. Government is gonna adjudicate. What's that conversation like? The example that he gave was, maybe was interpolate. If it's if it's information about baggage claims, you can you can use the Blockchain and crypt it and then only see the data at the other end. So that was actually, I thought, a good example. Why do you want to restrict that flow of data? But if it's personal information, keep it in country. But how is that conversation going with clients? >> Leo. Those can involve depending on the country, right and where you're at in the industry. >> But some Western countries are strict about that. >> Absolutely. And this is why we've created a platform that allows for data virtualization. We use Cooper nannies and technologies under the covers so that you can manage that in different locations. You could manage it across. Ah, hybrid of data centers or hybrid of public cloud vendors. And it allows you to still have one business application, and you can kind of do some of the separation and even separation of data. So there's there's, there's, there's an approach there, you know. But you gotta do a balance. Balance it. You gotta balance between innovation, digital transformation and how much you wanna, you know, govern so governs important. And then, you know. But for some projects, we may want to just quickly prototype. So there's a balance there, too. >> Well, that data virtualization tech is interesting because it gets the other piece, which was prevent data localization mandates. But if there is a mandate and we know that some countries aren't going to relax that mandate, you have, ah, a technical solution for that >> architecture that will support that. And that's a big investment for us right now. And where we're doing a lot of work in that space. Obviously, with red hat, you saw partnership or acquisition. So that's been >> really Yeah, I heard something about that's important. That's that's that's a big part of Chapter two. Yeah, all right. We'll give you the final world Caitlyn on the spring. I guess it's not spring it. Secondly, this summer, right? CDO event? >> No, it's been agreed. First day. So we kicked off. Today. We've got a full set of client panel's tomorrow. We've got some announcements around our meta data that I mentioned. Risk insights is a really cool offering. We'll be talking more about. We also have cognitive support. This is another one. Our clients that I really wanted to help with some of their support back in systems. So a lot of exciting announcements, new thought leadership coming out. It's been a great event and looking forward to the next next day. >> Well, I love the fact >> that you guys have have tied data science into the sea. Sweet roll. You guys have done a great job, I think, better than anybody in terms of of, of really advocating for the chief data officer. And this is a great event because it's piers talking. Appears a lot of private conversations going on. So congratulations on all the success and continued success worldwide. >> Thank you so much. Thank you, Dave. >> You welcome. Keep it right there, everybody. We'll be back with our next guest. Ready for this short break. We have a panel coming up. This is David. Dante. You're >> watching the Cube from IBM CDO right back.